res_namegrudzień 11, 2015
library(knitr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(ggExtra)
library(corrgram)
library(RColorBrewer)
library(caret)
library(pROC)
opts_chunk$set(echo=TRUE, cache=TRUE)
Powyżej znajduje się lista wykorzystanych bibliotek.
Dodatkowo ustawione zostały zmienne globalne, które mają zgeneralizować wyświetlanie fragmentów kodu i utrzymywaniu poszczególnych fragmentów w pamięci.
set.seed(23)
Powtarzalność badań zapeniła funkcja set.seed() z odpowiednio ustawionym ziarnem. ***
dane <- read.table(file = "all_summary.txt",
header = FALSE,
sep = ";",
dec = ".",
fill = FALSE, #czy usupełniać kolumny
col.names = strsplit(readLines("all_summary.txt", n = 1), ";")[[1]],
skip = 1,
na.strings = c('NAN'),
blank.lines.skip = TRUE, #Opóść linie puste
stringsAsFactors = TRUE
)
Do wczytania danych wykorzystana została funkcja read.table(). By dokonać dokładnego odwzorowania danych, znajdujących się w pliku tekstowym, do obiektu data.frame, ustawione zostały odpowiednie argumenty funkcji. ***
dane <- dane %>%
filter(!is.na(res_name),
!is.nan(res_name),
!res_name %in% c('DA','DC','DT','DU','DG','DI','UNK','UNX','UNL','PR','PD','Y1','EU','N','15P','UQ','PX4','NAN'))
Jako, że zmienna res_name w późniejszych badaniach posłużyła jakozmienna klasyfikująca dany ligard, musiała ona być odpowiednio identyfikowana przez określoną wartość. Mając to na uwadze, usunięte zostały wiersze, których wartość zmiennej res_name była niedostępna, czyli równa NA. ***
dane <- dane %>%
distinct(pdb_code, res_name)
kable(summary(dane))
| title | pdb_code | res_name | res_id | chain_id | local_BAa | local_NPa | local_Ra | local_RGa | local_SRGa | local_CCSa | local_CCPa | local_ZOa | local_ZDa | local_ZD_minus_a | local_ZD_plus_a | local_res_atom_count | local_res_atom_non_h_count | local_res_atom_non_h_occupancy_sum | local_res_atom_non_h_electron_sum | local_res_atom_non_h_electron_occupancy_sum | local_res_atom_C_count | local_res_atom_N_count | local_res_atom_O_count | local_res_atom_S_count | dict_atom_non_h_count | dict_atom_non_h_electron_sum | dict_atom_C_count | dict_atom_N_count | dict_atom_O_count | dict_atom_S_count | part_00_blob_electron_sum | part_00_blob_volume_sum | part_00_blob_parts | part_00_shape_O3 | part_00_shape_O4 | part_00_shape_O5 | part_00_shape_FL | part_00_shape_O3_norm | part_00_shape_O4_norm | part_00_shape_O5_norm | part_00_shape_FL_norm | part_00_shape_I1 | part_00_shape_I2 | part_00_shape_I3 | part_00_shape_I4 | part_00_shape_I5 | part_00_shape_I6 | part_00_shape_I1_norm | part_00_shape_I2_norm | part_00_shape_I3_norm | part_00_shape_I4_norm | part_00_shape_I5_norm | part_00_shape_I6_norm | part_00_shape_I1_scaled | part_00_shape_I2_scaled | part_00_shape_I3_scaled | part_00_shape_I4_scaled | part_00_shape_I5_scaled | part_00_shape_I6_scaled | part_00_shape_M000 | part_00_shape_E3_E1 | part_00_shape_E2_E1 | part_00_shape_E3_E2 | part_00_shape_sqrt_E1 | part_00_shape_sqrt_E2 | part_00_shape_sqrt_E3 | part_00_density_O3 | part_00_density_O4 | part_00_density_O5 | part_00_density_FL | part_00_density_O3_norm | part_00_density_O4_norm | part_00_density_O5_norm | part_00_density_FL_norm | part_00_density_I1 | part_00_density_I2 | part_00_density_I3 | part_00_density_I4 | part_00_density_I5 | part_00_density_I6 | part_00_density_I1_norm | part_00_density_I2_norm | part_00_density_I3_norm | part_00_density_I4_norm | part_00_density_I5_norm | part_00_density_I6_norm | part_00_density_I1_scaled | part_00_density_I2_scaled | part_00_density_I3_scaled | part_00_density_I4_scaled | part_00_density_I5_scaled | part_00_density_I6_scaled | part_00_density_M000 | part_00_density_E3_E1 | part_00_density_E2_E1 | part_00_density_E3_E2 | part_00_density_sqrt_E1 | part_00_density_sqrt_E2 | part_00_density_sqrt_E3 | part_01_blob_electron_sum | part_01_blob_volume_sum | part_01_blob_parts | part_01_shape_O3 | part_01_shape_O4 | part_01_shape_O5 | part_01_shape_FL | part_01_shape_O3_norm | part_01_shape_O4_norm | part_01_shape_O5_norm | part_01_shape_FL_norm | part_01_shape_I1 | part_01_shape_I2 | part_01_shape_I3 | part_01_shape_I4 | part_01_shape_I5 | part_01_shape_I6 | part_01_shape_I1_norm | part_01_shape_I2_norm | part_01_shape_I3_norm | part_01_shape_I4_norm | part_01_shape_I5_norm | part_01_shape_I6_norm | part_01_shape_I1_scaled | part_01_shape_I2_scaled | part_01_shape_I3_scaled | part_01_shape_I4_scaled | part_01_shape_I5_scaled | part_01_shape_I6_scaled | part_01_shape_M000 | part_01_shape_E3_E1 | part_01_shape_E2_E1 | part_01_shape_E3_E2 | part_01_shape_sqrt_E1 | part_01_shape_sqrt_E2 | part_01_shape_sqrt_E3 | part_01_density_O3 | part_01_density_O4 | part_01_density_O5 | part_01_density_FL | part_01_density_O3_norm | part_01_density_O4_norm | part_01_density_O5_norm | part_01_density_FL_norm | part_01_density_I1 | part_01_density_I2 | part_01_density_I3 | part_01_density_I4 | part_01_density_I5 | part_01_density_I6 | part_01_density_I1_norm | part_01_density_I2_norm | part_01_density_I3_norm | part_01_density_I4_norm | part_01_density_I5_norm | part_01_density_I6_norm | part_01_density_I1_scaled | part_01_density_I2_scaled | part_01_density_I3_scaled | part_01_density_I4_scaled | part_01_density_I5_scaled | part_01_density_I6_scaled | part_01_density_M000 | part_01_density_E3_E1 | part_01_density_E2_E1 | part_01_density_E3_E2 | part_01_density_sqrt_E1 | part_01_density_sqrt_E2 | part_01_density_sqrt_E3 | part_02_blob_electron_sum | part_02_blob_volume_sum | part_02_blob_parts | part_02_shape_O3 | part_02_shape_O4 | part_02_shape_O5 | part_02_shape_FL | part_02_shape_O3_norm | part_02_shape_O4_norm | part_02_shape_O5_norm | part_02_shape_FL_norm | part_02_shape_I1 | part_02_shape_I2 | part_02_shape_I3 | part_02_shape_I4 | part_02_shape_I5 | part_02_shape_I6 | part_02_shape_I1_norm | part_02_shape_I2_norm | part_02_shape_I3_norm | part_02_shape_I4_norm | part_02_shape_I5_norm | part_02_shape_I6_norm | part_02_shape_I1_scaled | part_02_shape_I2_scaled | part_02_shape_I3_scaled | part_02_shape_I4_scaled | part_02_shape_I5_scaled | part_02_shape_I6_scaled | part_02_shape_M000 | part_02_shape_E3_E1 | part_02_shape_E2_E1 | part_02_shape_E3_E2 | part_02_shape_sqrt_E1 | part_02_shape_sqrt_E2 | part_02_shape_sqrt_E3 | part_02_density_O3 | part_02_density_O4 | part_02_density_O5 | part_02_density_FL | part_02_density_O3_norm | part_02_density_O4_norm | part_02_density_O5_norm | part_02_density_FL_norm | part_02_density_I1 | part_02_density_I2 | part_02_density_I3 | part_02_density_I4 | part_02_density_I5 | part_02_density_I6 | part_02_density_I1_norm | part_02_density_I2_norm | part_02_density_I3_norm | part_02_density_I4_norm | part_02_density_I5_norm | part_02_density_I6_norm | part_02_density_I1_scaled | part_02_density_I2_scaled | part_02_density_I3_scaled | part_02_density_I4_scaled | part_02_density_I5_scaled | part_02_density_I6_scaled | part_02_density_M000 | part_02_density_E3_E1 | part_02_density_E2_E1 | part_02_density_E3_E2 | part_02_density_sqrt_E1 | part_02_density_sqrt_E2 | part_02_density_sqrt_E3 | part_03_blob_electron_sum | part_03_blob_volume_sum | part_03_blob_parts | part_03_shape_O3 | part_03_shape_O4 | part_03_shape_O5 | part_03_shape_FL | part_03_shape_O3_norm | part_03_shape_O4_norm | part_03_shape_O5_norm | part_03_shape_FL_norm | part_03_shape_I1 | part_03_shape_I2 | part_03_shape_I3 | part_03_shape_I4 | part_03_shape_I5 | part_03_shape_I6 | part_03_shape_I1_norm | part_03_shape_I2_norm | part_03_shape_I3_norm | part_03_shape_I4_norm | part_03_shape_I5_norm | part_03_shape_I6_norm | part_03_shape_I1_scaled | part_03_shape_I2_scaled | part_03_shape_I3_scaled | part_03_shape_I4_scaled | part_03_shape_I5_scaled | part_03_shape_I6_scaled | part_03_shape_M000 | part_03_shape_E3_E1 | part_03_shape_E2_E1 | part_03_shape_E3_E2 | part_03_shape_sqrt_E1 | part_03_shape_sqrt_E2 | part_03_shape_sqrt_E3 | part_03_density_O3 | part_03_density_O4 | part_03_density_O5 | part_03_density_FL | part_03_density_O3_norm | part_03_density_O4_norm | part_03_density_O5_norm | part_03_density_FL_norm | part_03_density_I1 | part_03_density_I2 | part_03_density_I3 | part_03_density_I4 | part_03_density_I5 | part_03_density_I6 | part_03_density_I1_norm | part_03_density_I2_norm | part_03_density_I3_norm | part_03_density_I4_norm | part_03_density_I5_norm | part_03_density_I6_norm | part_03_density_I1_scaled | part_03_density_I2_scaled | part_03_density_I3_scaled | part_03_density_I4_scaled | part_03_density_I5_scaled | part_03_density_I6_scaled | part_03_density_M000 | part_03_density_E3_E1 | part_03_density_E2_E1 | part_03_density_E3_E2 | part_03_density_sqrt_E1 | part_03_density_sqrt_E2 | part_03_density_sqrt_E3 | part_04_blob_electron_sum | part_04_blob_volume_sum | part_04_blob_parts | part_04_shape_O3 | part_04_shape_O4 | part_04_shape_O5 | part_04_shape_FL | part_04_shape_O3_norm | part_04_shape_O4_norm | part_04_shape_O5_norm | part_04_shape_FL_norm | part_04_shape_I1 | part_04_shape_I2 | part_04_shape_I3 | part_04_shape_I4 | part_04_shape_I5 | part_04_shape_I6 | part_04_shape_I1_norm | part_04_shape_I2_norm | part_04_shape_I3_norm | part_04_shape_I4_norm | part_04_shape_I5_norm | part_04_shape_I6_norm | part_04_shape_I1_scaled | part_04_shape_I2_scaled | part_04_shape_I3_scaled | part_04_shape_I4_scaled | part_04_shape_I5_scaled | part_04_shape_I6_scaled | part_04_shape_M000 | part_04_shape_E3_E1 | part_04_shape_E2_E1 | part_04_shape_E3_E2 | part_04_shape_sqrt_E1 | part_04_shape_sqrt_E2 | part_04_shape_sqrt_E3 | part_04_density_O3 | part_04_density_O4 | part_04_density_O5 | part_04_density_FL | part_04_density_O3_norm | part_04_density_O4_norm | part_04_density_O5_norm | part_04_density_FL_norm | part_04_density_I1 | part_04_density_I2 | part_04_density_I3 | part_04_density_I4 | part_04_density_I5 | part_04_density_I6 | part_04_density_I1_norm | part_04_density_I2_norm | part_04_density_I3_norm | part_04_density_I4_norm | part_04_density_I5_norm | part_04_density_I6_norm | part_04_density_I1_scaled | part_04_density_I2_scaled | part_04_density_I3_scaled | part_04_density_I4_scaled | part_04_density_I5_scaled | part_04_density_I6_scaled | part_04_density_M000 | part_04_density_E3_E1 | part_04_density_E2_E1 | part_04_density_E3_E2 | part_04_density_sqrt_E1 | part_04_density_sqrt_E2 | part_04_density_sqrt_E3 | part_05_blob_electron_sum | part_05_blob_volume_sum | part_05_blob_parts | part_05_shape_O3 | part_05_shape_O4 | part_05_shape_O5 | part_05_shape_FL | part_05_shape_O3_norm | part_05_shape_O4_norm | part_05_shape_O5_norm | part_05_shape_FL_norm | part_05_shape_I1 | part_05_shape_I2 | part_05_shape_I3 | part_05_shape_I4 | part_05_shape_I5 | part_05_shape_I6 | part_05_shape_I1_norm | part_05_shape_I2_norm | part_05_shape_I3_norm | part_05_shape_I4_norm | part_05_shape_I5_norm | part_05_shape_I6_norm | part_05_shape_I1_scaled | part_05_shape_I2_scaled | part_05_shape_I3_scaled | part_05_shape_I4_scaled | part_05_shape_I5_scaled | part_05_shape_I6_scaled | part_05_shape_M000 | part_05_shape_E3_E1 | part_05_shape_E2_E1 | part_05_shape_E3_E2 | part_05_shape_sqrt_E1 | part_05_shape_sqrt_E2 | part_05_shape_sqrt_E3 | part_05_density_O3 | part_05_density_O4 | part_05_density_O5 | part_05_density_FL | part_05_density_O3_norm | part_05_density_O4_norm | part_05_density_O5_norm | part_05_density_FL_norm | part_05_density_I1 | part_05_density_I2 | part_05_density_I3 | part_05_density_I4 | part_05_density_I5 | part_05_density_I6 | part_05_density_I1_norm | part_05_density_I2_norm | part_05_density_I3_norm | part_05_density_I4_norm | part_05_density_I5_norm | part_05_density_I6_norm | part_05_density_I1_scaled | part_05_density_I2_scaled | part_05_density_I3_scaled | part_05_density_I4_scaled | part_05_density_I5_scaled | part_05_density_I6_scaled | part_05_density_M000 | part_05_density_E3_E1 | part_05_density_E2_E1 | part_05_density_E3_E2 | part_05_density_sqrt_E1 | part_05_density_sqrt_E2 | part_05_density_sqrt_E3 | part_06_blob_electron_sum | part_06_blob_volume_sum | part_06_blob_parts | part_06_shape_O3 | part_06_shape_O4 | part_06_shape_O5 | part_06_shape_FL | part_06_shape_O3_norm | part_06_shape_O4_norm | part_06_shape_O5_norm | part_06_shape_FL_norm | part_06_shape_I1 | part_06_shape_I2 | part_06_shape_I3 | part_06_shape_I4 | part_06_shape_I5 | part_06_shape_I6 | part_06_shape_I1_norm | part_06_shape_I2_norm | part_06_shape_I3_norm | part_06_shape_I4_norm | part_06_shape_I5_norm | part_06_shape_I6_norm | part_06_shape_I1_scaled | part_06_shape_I2_scaled | part_06_shape_I3_scaled | part_06_shape_I4_scaled | part_06_shape_I5_scaled | part_06_shape_I6_scaled | part_06_shape_M000 | part_06_shape_E3_E1 | part_06_shape_E2_E1 | part_06_shape_E3_E2 | part_06_shape_sqrt_E1 | part_06_shape_sqrt_E2 | part_06_shape_sqrt_E3 | part_06_density_O3 | part_06_density_O4 | part_06_density_O5 | part_06_density_FL | part_06_density_O3_norm | part_06_density_O4_norm | part_06_density_O5_norm | part_06_density_FL_norm | part_06_density_I1 | part_06_density_I2 | part_06_density_I3 | part_06_density_I4 | part_06_density_I5 | part_06_density_I6 | part_06_density_I1_norm | part_06_density_I2_norm | part_06_density_I3_norm | part_06_density_I4_norm | part_06_density_I5_norm | part_06_density_I6_norm | part_06_density_I1_scaled | part_06_density_I2_scaled | part_06_density_I3_scaled | part_06_density_I4_scaled | part_06_density_I5_scaled | part_06_density_I6_scaled | part_06_density_M000 | part_06_density_E3_E1 | part_06_density_E2_E1 | part_06_density_E3_E2 | part_06_density_sqrt_E1 | part_06_density_sqrt_E2 | part_06_density_sqrt_E3 | part_07_blob_electron_sum | part_07_blob_volume_sum | part_07_blob_parts | part_07_shape_O3 | part_07_shape_O4 | part_07_shape_O5 | part_07_shape_FL | part_07_shape_O3_norm | part_07_shape_O4_norm | part_07_shape_O5_norm | part_07_shape_FL_norm | part_07_shape_I1 | part_07_shape_I2 | part_07_shape_I3 | part_07_shape_I4 | part_07_shape_I5 | part_07_shape_I6 | part_07_shape_I1_norm | part_07_shape_I2_norm | part_07_shape_I3_norm | part_07_shape_I4_norm | part_07_shape_I5_norm | part_07_shape_I6_norm | part_07_shape_I1_scaled | part_07_shape_I2_scaled | part_07_shape_I3_scaled | part_07_shape_I4_scaled | part_07_shape_I5_scaled | part_07_shape_I6_scaled | part_07_shape_M000 | part_07_shape_E3_E1 | part_07_shape_E2_E1 | part_07_shape_E3_E2 | part_07_shape_sqrt_E1 | part_07_shape_sqrt_E2 | part_07_shape_sqrt_E3 | part_07_density_O3 | part_07_density_O4 | part_07_density_O5 | part_07_density_FL | part_07_density_O3_norm | part_07_density_O4_norm | part_07_density_O5_norm | part_07_density_FL_norm | part_07_density_I1 | part_07_density_I2 | part_07_density_I3 | part_07_density_I4 | part_07_density_I5 | part_07_density_I6 | part_07_density_I1_norm | part_07_density_I2_norm | part_07_density_I3_norm | part_07_density_I4_norm | part_07_density_I5_norm | part_07_density_I6_norm | part_07_density_I1_scaled | part_07_density_I2_scaled | part_07_density_I3_scaled | part_07_density_I4_scaled | part_07_density_I5_scaled | part_07_density_I6_scaled | part_07_density_M000 | part_07_density_E3_E1 | part_07_density_E2_E1 | part_07_density_E3_E2 | part_07_density_sqrt_E1 | part_07_density_sqrt_E2 | part_07_density_sqrt_E3 | part_08_blob_electron_sum | part_08_blob_volume_sum | part_08_blob_parts | part_08_shape_O3 | part_08_shape_O4 | part_08_shape_O5 | part_08_shape_FL | part_08_shape_O3_norm | part_08_shape_O4_norm | part_08_shape_O5_norm | part_08_shape_FL_norm | part_08_shape_I1 | part_08_shape_I2 | part_08_shape_I3 | part_08_shape_I4 | part_08_shape_I5 | part_08_shape_I6 | part_08_shape_I1_norm | part_08_shape_I2_norm | part_08_shape_I3_norm | part_08_shape_I4_norm | part_08_shape_I5_norm | part_08_shape_I6_norm | part_08_shape_I1_scaled | part_08_shape_I2_scaled | part_08_shape_I3_scaled | part_08_shape_I4_scaled | part_08_shape_I5_scaled | part_08_shape_I6_scaled | part_08_shape_M000 | part_08_shape_E3_E1 | part_08_shape_E2_E1 | part_08_shape_E3_E2 | part_08_shape_sqrt_E1 | part_08_shape_sqrt_E2 | part_08_shape_sqrt_E3 | part_08_density_O3 | part_08_density_O4 | part_08_density_O5 | part_08_density_FL | part_08_density_O3_norm | part_08_density_O4_norm | part_08_density_O5_norm | part_08_density_FL_norm | part_08_density_I1 | part_08_density_I2 | part_08_density_I3 | part_08_density_I4 | part_08_density_I5 | part_08_density_I6 | part_08_density_I1_norm | part_08_density_I2_norm | part_08_density_I3_norm | part_08_density_I4_norm | part_08_density_I5_norm | part_08_density_I6_norm | part_08_density_I1_scaled | part_08_density_I2_scaled | part_08_density_I3_scaled | part_08_density_I4_scaled | part_08_density_I5_scaled | part_08_density_I6_scaled | part_08_density_M000 | part_08_density_E3_E1 | part_08_density_E2_E1 | part_08_density_E3_E2 | part_08_density_sqrt_E1 | part_08_density_sqrt_E2 | part_08_density_sqrt_E3 | part_09_blob_electron_sum | part_09_blob_volume_sum | part_09_blob_parts | part_09_shape_O3 | part_09_shape_O4 | part_09_shape_O5 | part_09_shape_FL | part_09_shape_O3_norm | part_09_shape_O4_norm | part_09_shape_O5_norm | part_09_shape_FL_norm | part_09_shape_I1 | part_09_shape_I2 | part_09_shape_I3 | part_09_shape_I4 | part_09_shape_I5 | part_09_shape_I6 | part_09_shape_I1_norm | part_09_shape_I2_norm | part_09_shape_I3_norm | part_09_shape_I4_norm | part_09_shape_I5_norm | part_09_shape_I6_norm | part_09_shape_I1_scaled | part_09_shape_I2_scaled | part_09_shape_I3_scaled | part_09_shape_I4_scaled | part_09_shape_I5_scaled | part_09_shape_I6_scaled | part_09_shape_M000 | part_09_shape_E3_E1 | part_09_shape_E2_E1 | part_09_shape_E3_E2 | part_09_shape_sqrt_E1 | part_09_shape_sqrt_E2 | part_09_shape_sqrt_E3 | part_09_density_O3 | part_09_density_O4 | part_09_density_O5 | part_09_density_FL | part_09_density_O3_norm | part_09_density_O4_norm | part_09_density_O5_norm | part_09_density_FL_norm | part_09_density_I1 | part_09_density_I2 | part_09_density_I3 | part_09_density_I4 | part_09_density_I5 | part_09_density_I6 | part_09_density_I1_norm | part_09_density_I2_norm | part_09_density_I3_norm | part_09_density_I4_norm | part_09_density_I5_norm | part_09_density_I6_norm | part_09_density_I1_scaled | part_09_density_I2_scaled | part_09_density_I3_scaled | part_09_density_I4_scaled | part_09_density_I5_scaled | part_09_density_I6_scaled | part_09_density_M000 | part_09_density_E3_E1 | part_09_density_E2_E1 | part_09_density_E3_E2 | part_09_density_sqrt_E1 | part_09_density_sqrt_E2 | part_09_density_sqrt_E3 | local_volume | local_electrons | local_mean | local_std | local_min | local_max | local_skewness | local_parts | fo_col | fc_col | weight_col | grid_space | solvent_radius | solvent_opening_radius | resolution_max_limit | resolution | TwoFoFc_mean | TwoFoFc_std | TwoFoFc_square_std | TwoFoFc_min | TwoFoFc_max | Fo_mean | Fo_std | Fo_square_std | Fo_min | Fo_max | FoFc_mean | FoFc_std | FoFc_square_std | FoFc_min | FoFc_max | Fc_mean | Fc_std | Fc_square_std | Fc_min | Fc_max | solvent_mask_count | void_mask_count | modeled_mask_count | solvent_ratio | TwoFoFc_bulk_mean | TwoFoFc_bulk_std | TwoFoFc_void_mean | TwoFoFc_void_std | TwoFoFc_modeled_mean | TwoFoFc_modeled_std | Fo_bulk_mean | Fo_bulk_std | Fo_void_mean | Fo_void_std | Fo_modeled_mean | Fo_modeled_std | FoFc_bulk_mean | FoFc_bulk_std | FoFc_void_mean | FoFc_void_std | FoFc_modeled_mean | FoFc_modeled_std | Fc_bulk_mean | Fc_bulk_std | Fc_void_mean | Fc_void_std | Fc_modeled_mean | Fc_modeled_std | TwoFoFc_void_fit_binormal_mean1 | TwoFoFc_void_fit_binormal_std1 | TwoFoFc_void_fit_binormal_mean2 | TwoFoFc_void_fit_binormal_std2 | TwoFoFc_void_fit_binormal_scale | TwoFoFc_solvent_fit_normal_mean | TwoFoFc_solvent_fit_normal_std | part_step_FoFc_std_min | part_step_FoFc_std_max | part_step_FoFc_std_step | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110l BME 901 A: 1 | 3ag4 : 16 | SO4 :1183 | 301 : 430 | A :8630 | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : NA | Min. : 1.00 | Min. : 1.00 | Min. : 0.00 | Min. : 3.0 | Min. : 0.00 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. :0.0000 | Min. : 1.00 | Min. : 3.0 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 0.000 | Min. : 0.00 | Min. :0.0000 | Min. : 24253 | Min. :1.952e+08 | Min. :5.160e+11 | Min. :3.330e+06 | Min. :0.2312 | Min. :0.0178 | Min. :0.0005 | Min. :0.0000 | Min. :6.875e+05 | Min. :1.246e+11 | Min. :9.688e+10 | Min. :1.335e+06 | Min. :4.728e+03 | Min. :5.618e+09 | Min. : 0.0637 | Min. : 0.0011 | Min. : 0.0008 | Min. :0.0000 | Min. :0.0000 | Min. : 0.0049 | Min. :0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0e+00 | Min. :0 | Min. : 1023 | Min. :0.0005 | Min. :0.0142 | Min. :0.0042 | Min. : 2.930 | Min. : 1.793 | Min. : 0.498 | Min. : 1527 | Min. :7.096e+05 | Min. :1.011e+08 | Min. :9.595e+05 | Min. :0.0593 | Min. :0.0012 | Min. :0.0000 | Min. : 0.0000 | Min. :6.548e+04 | Min. :9.611e+08 | Min. :1.275e+09 | Min. :4.130e+05 | Min. :2.366e+04 | Min. :3.899e+07 | Min. : 0.0048 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. : 47 | Min. :0.0007 | Min. :0.0133 | Min. :0.0052 | Min. : 2.499 | Min. : 1.769 | Min. : 0.4972 | Min. : 0.000 | Min. : 0.00 | Min. :0.0000 | Min. : 24186 | Min. :1.947e+08 | Min. :5.217e+11 | Min. :2.023e+06 | Min. :0.2313 | Min. :0.0178 | Min. :0.0005 | Min. :0.0000 | Min. :6.807e+05 | Min. :1.231e+11 | Min. :9.354e+10 | Min. :8.184e+05 | Min. :3.360e+03 | Min. :5.502e+09 | Min. : 0.0637 | Min. : 0.0011 | Min. : 0.0008 | Min. :0.0000 | Min. :0.0000 | Min. : 0.0049 | Min. :0.0001 | Min. :0.0000 | Min. :0.000 | Min. :0.0000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.0005 | Min. :0.0127 | Min. :0.0044 | Min. : 2.898 | Min. : 1.947 | Min. : 0.4978 | Min. : 1038 | Min. :3.280e+05 | Min. :3.182e+07 | Min. :3.230e+05 | Min. :0.0592 | Min. :0.0012 | Min. :0.0000 | Min. : 0.0000 | Min. :3.748e+04 | Min. :3.151e+08 | Min. :4.195e+08 | Min. :1.414e+05 | Min. :1.786e+04 | Min. :1.512e+07 | Min. : 0.0048 | Min. : 0.0000 | Min. : 0.000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. : 37.76 | Min. :0.0007 | Min. :0.0122 | Min. :0.0054 | Min. : 2.496 | Min. : 1.877 | Min. : 0.4971 | Min. : 0.00 | Min. : 0.000 | Min. :0.0000 | Min. : 24331 | Min. :1.955e+08 | Min. :5.180e+11 | Min. :2.142e+06 | Min. :0.2312 | Min. :0.0178 | Min. :0.0005 | Min. : 0.0000 | Min. :6.937e+05 | Min. :1.254e+11 | Min. :1.013e+11 | Min. :8.662e+05 | Min. :3.121e+03 | Min. :5.751e+09 | Min. : 0.0637 | Min. : 0.0011 | Min. : 0.0008 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0049 | Min. :0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0 | Min. : 1023 | Min. :0.0006 | Min. :0.0138 | Min. :0.0047 | Min. : 2.917 | Min. : 1.914 | Min. : 0.4957 | Min. : 4269 | Min. :5.654e+06 | Min. :2.385e+09 | Min. :1.443e+06 | Min. :0.0588 | Min. :0.0011 | Min. :0.0000 | Min. : 0.0000 | Min. :1.324e+05 | Min. :3.851e+09 | Min. :4.897e+09 | Min. :6.988e+05 | Min. :1.294e+04 | Min. :2.080e+08 | Min. : 0.0047 | Min. : 0.0000 | Min. : 0.000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. : 177.5 | Min. :0.0008 | Min. :0.0138 | Min. :0.0058 | Min. : 2.487 | Min. : 1.857 | Min. : 0.4957 | Min. : 0.0 | Min. : 0.00 | Min. :0.0000 | Min. : 24164 | Min. :1.941e+08 | Min. :5.143e+11 | Min. :6.027e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.790e+05 | Min. :1.225e+11 | Min. :9.315e+10 | Min. :2.433e+06 | Min. :1.301e+03 | Min. :5.484e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.012 | Min. :0.005 | Min. : 2.884 | Min. : 1.817 | Min. : 0.493 | Min. : 4027 | Min. :5.262e+06 | Min. :2.243e+09 | Min. :1.389e+06 | Min. : 0.058 | Min. : 0.001 | Min. :0.000 | Min. : 0.000 | Min. :1.186e+05 | Min. :3.489e+09 | Min. :3.239e+09 | Min. :5.853e+05 | Min. :1.301e+04 | Min. :1.669e+08 | Min. : 0.005 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. : 0.000 | Min. :0.00 | Min. :0.000 | Min. :0.000 | Min. : 168 | Min. :0.001 | Min. :0.011 | Min. :0.006 | Min. : 2.473 | Min. : 1.773 | Min. : 0.494 | Min. : 0.000 | Min. : 0.0 | Min. :0.0000 | Min. : 24198 | Min. :1.946e+08 | Min. :5.198e+11 | Min. :7.913e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.817e+05 | Min. :1.231e+11 | Min. :9.400e+10 | Min. :3.183e+06 | Min. :3.774e+03 | Min. :5.528e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.009 | Min. :0.010 | Min. : 2.869 | Min. : 1.644 | Min. : 0.496 | Min. : 6632 | Min. :1.333e+07 | Min. :7.807e+09 | Min. :1.426e+06 | Min. : 0.058 | Min. : 0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.208e+05 | Min. :1.088e+10 | Min. :1.331e+10 | Min. :5.746e+05 | Min. :3.000e+00 | Min. :5.871e+08 | Min. : 0.005 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. : 260.4 | Min. :0.001 | Min. :0.008 | Min. :0.011 | Min. : 2.577 | Min. : 1.617 | Min. : 0.497 | Min. : 0.000 | Min. : 0.000 | Min. :0.00 | Min. : 24236 | Min. :1.956e+08 | Min. :5.224e+11 | Min. :4.696e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.861e+05 | Min. :1.254e+11 | Min. :9.477e+10 | Min. :1.895e+06 | Min. :5.507e+03 | Min. :5.553e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.008 | Min. :0.011 | Min. : 2.866 | Min. : 2.056 | Min. : 0.496 | Min. : 6155 | Min. :1.161e+07 | Min. :6.600e+09 | Min. :8.006e+05 | Min. : 0.058 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :1.955e+05 | Min. :9.552e+09 | Min. :8.451e+09 | Min. :3.227e+05 | Min. :8.210e+02 | Min. :4.207e+08 | Min. : 0.005 | Min. : 0.000 | Min. : 0.0 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. : 234.8 | Min. :0.001 | Min. :0.008 | Min. :0.013 | Min. : 2.538 | Min. : 1.980 | Min. : 0.496 | Min. : 0.00 | Min. : 0.00 | Min. :0.0000 | Min. : 24358 | Min. :1.971e+08 | Min. :5.177e+11 | Min. :7.396e+06 | Min. : 0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.850e+05 | Min. :1.250e+11 | Min. :9.408e+10 | Min. :2.975e+06 | Min. :4.763e+03 | Min. :5.566e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.008 | Min. :0.012 | Min. : 2.853 | Min. : 2.037 | Min. : 0.496 | Min. : 6900 | Min. :1.522e+07 | Min. :1.075e+10 | Min. :2.231e+06 | Min. : 0.057 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.044e+05 | Min. :1.033e+10 | Min. :1.034e+10 | Min. :8.939e+05 | Min. :2.507e+03 | Min. :5.106e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0.000 | Min. : 292.1 | Min. :0.001 | Min. :0.007 | Min. :0.014 | Min. : 2.523 | Min. : 1.955 | Min. : 0.496 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 24313 | Min. :1.960e+08 | Min. :5.149e+11 | Min. :5.901e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.911e+05 | Min. :1.255e+11 | Min. :9.856e+10 | Min. :2.436e+06 | Min. :2.860e+03 | Min. :5.679e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.006 | Min. :0.007 | Min. :0.018 | Min. : 2.930 | Min. : 1.947 | Min. : 1.077 | Min. : 7722 | Min. :1.917e+07 | Min. :1.546e+10 | Min. :2.261e+06 | Min. :0.057 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.348e+05 | Min. :1.442e+10 | Min. :1.161e+10 | Min. :9.168e+05 | Min. :6.445e+03 | Min. :6.453e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 291.9 | Min. :0.005 | Min. :0.007 | Min. :0.018 | Min. : 2.610 | Min. : 1.921 | Min. : 1.063 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 24284 | Min. :1.955e+08 | Min. :5.167e+11 | Min. :4.143e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.883e+05 | Min. :1.246e+11 | Min. :9.780e+10 | Min. :1.767e+06 | Min. :5.470e+02 | Min. :5.625e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.005 | Min. :0.010 | Min. :0.020 | Min. : 2.942 | Min. : 2.154 | Min. : 1.051 | Min. : 6023 | Min. :1.180e+07 | Min. :7.555e+09 | Min. :1.376e+06 | Min. :0.057 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :1.750e+05 | Min. :7.599e+09 | Min. :7.022e+09 | Min. :5.570e+05 | Min. :8.525e+03 | Min. :3.736e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0 | Min. : 256.5 | Min. :0.004 | Min. :0.010 | Min. :0.020 | Min. : 2.620 | Min. : 2.106 | Min. : 1.041 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 24584 | Min. :1.970e+08 | Min. :5.129e+11 | Min. :4.052e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :7.117e+05 | Min. :1.303e+11 | Min. :1.047e+11 | Min. :1.669e+06 | Min. :3.899e+03 | Min. :5.953e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0 | Min. : 1023 | Min. :0.004 | Min. :0.011 | Min. :0.025 | Min. : 2.949 | Min. : 2.144 | Min. : 1.060 | Min. : 7987 | Min. :2.102e+07 | Min. :1.388e+10 | Min. :1.586e+06 | Min. : 0.056 | Min. : 0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.621e+05 | Min. :1.642e+10 | Min. :1.529e+10 | Min. :6.442e+05 | Min. :1.606e+04 | Min. :7.249e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. : 297.4 | Min. :0.004 | Min. :0.010 | Min. :0.025 | Min. : 2.598 | Min. : 2.108 | Min. : 1.045 | Min. : 64.77 | Min. : 0.000 | Min. :0.00000 | Min. :0.0000 | Min. :0 | Min. : 0.000 | Min. :0.0000 | Min. :0.0000 | DELFWT:14132 | PHDELWT:14132 | Mode:logical | Min. :0.2 | Min. :1.9 | Min. :1.4 | Min. :2 | Min. :0.800 | Min. :-2.992e-06 | Min. :0.009962 | Min. :0.0003424 | Min. :-2.3692 | Min. : 0.2245 | Min. :-3.028e-06 | Min. :0.01847 | Min. :0.003589 | Min. :-3.3935 | Min. : 0.2999 | Min. :-2.206e-06 | Min. :0.009009 | Min. :0.0001201 | Min. :-26.24490 | Min. : 0.07625 | Min. :-7.867e-07 | Min. :0.005332 | Min. :0.000403 | Min. :-3.2550 | Min. : 0.2407 | Min. : 0 | Min. : 5472 | Min. : 15008 | Min. :0.0000 | Min. :-0.30666 | Min. :0.00930 | Min. :-0.2850280 | Min. :0.02783 | Min. :6.595e-05 | Min. :0.03485 | Min. :-0.08705 | Min. :0.01120 | Min. :-0.24222 | Min. :0.03827 | Min. :-0.003379 | Min. :0.05494 | Min. :-0.08982 | Min. :0.00893 | Min. :-0.095296 | Min. :0.01336 | Min. :-0.11559 | Min. :0.01705 | Min. :-0.21683 | Min. :0.00345 | Min. :-0.239831 | Min. :0.01529 | Min. :-0.005495 | Min. :0.02092 | Min. :-0.66520 | Min. :0.01075 | Min. :0.000e+00 | Min. :0.000e+00 | Min. :-11.2668 | Min. :-0.31511 | Min. :0.00918 | Min. :2.5 | Min. :7.1 | Min. :0.5 | |
| 111l CL 173 A : 1 | 3abl : 15 | ZN : 849 | 1 : 424 | B :2905 | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: NA | 1st Qu.: 1.00 | 1st Qu.: 1.00 | 1st Qu.: 1.00 | 1st Qu.: 30.0 | 1st Qu.: 30.00 | 1st Qu.: 0.000 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 1.00 | 1st Qu.: 30.0 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 9.469 | 1st Qu.: 14.44 | 1st Qu.:1.0000 | 1st Qu.: 106958 | 1st Qu.:2.876e+09 | 1st Qu.:2.121e+13 | 1st Qu.:4.427e+10 | 1st Qu.:0.3173 | 1st Qu.:0.0275 | 1st Qu.:0.0007 | 1st Qu.:0.0021 | 1st Qu.:8.630e+06 | 1st Qu.:1.181e+13 | 1st Qu.:2.797e+13 | 1st Qu.:2.340e+10 | 1st Qu.:5.138e+09 | 1st Qu.:4.161e+11 | 1st Qu.: 0.1478 | 1st Qu.: 0.0037 | 1st Qu.: 0.0082 | 1st Qu.:0.0011 | 1st Qu.:0.0002 | 1st Qu.: 0.0202 | 1st Qu.:0.0007 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0e+00 | 1st Qu.:0 | 1st Qu.: 1900 | 1st Qu.:0.0715 | 1st Qu.:0.1974 | 1st Qu.:0.3046 | 1st Qu.: 5.594 | 1st Qu.: 3.546 | 1st Qu.: 2.654 | 1st Qu.: 62839 | 1st Qu.:1.048e+09 | 1st Qu.:4.744e+12 | 1st Qu.:1.340e+10 | 1st Qu.:0.3736 | 1st Qu.:0.0376 | 1st Qu.:0.0011 | 1st Qu.: 0.0033 | 1st Qu.:4.534e+06 | 1st Qu.:3.276e+12 | 1st Qu.:7.603e+12 | 1st Qu.:7.514e+09 | 1st Qu.:2.054e+09 | 1st Qu.:1.222e+11 | 1st Qu.: 0.2201 | 1st Qu.: 0.0084 | 1st Qu.: 0.0169 | 1st Qu.: 0.0020 | 1st Qu.: 0.0006 | 1st Qu.: 0.0343 | 1st Qu.:0.0014 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.: 1263 | 1st Qu.:0.0678 | 1st Qu.:0.1912 | 1st Qu.:0.2997 | 1st Qu.: 5.086 | 1st Qu.: 3.263 | 1st Qu.: 2.4659 | 1st Qu.: 8.581 | 1st Qu.: 12.77 | 1st Qu.:1.0000 | 1st Qu.: 99934 | 1st Qu.:2.480e+09 | 1st Qu.:1.702e+13 | 1st Qu.:3.533e+10 | 1st Qu.:0.3104 | 1st Qu.:0.0267 | 1st Qu.:0.0007 | 1st Qu.:0.0019 | 1st Qu.:7.759e+06 | 1st Qu.:9.470e+12 | 1st Qu.:2.146e+13 | 1st Qu.:1.857e+10 | 1st Qu.:3.874e+09 | 1st Qu.:3.399e+11 | 1st Qu.: 0.1405 | 1st Qu.: 0.0034 | 1st Qu.: 0.0072 | 1st Qu.:0.0010 | 1st Qu.:0.0002 | 1st Qu.: 0.0184 | 1st Qu.:0.0007 | 1st Qu.:0.0000 | 1st Qu.:0.000 | 1st Qu.:0.0000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1829 | 1st Qu.:0.0687 | 1st Qu.:0.1929 | 1st Qu.:0.2994 | 1st Qu.: 5.444 | 1st Qu.: 3.499 | 1st Qu.: 2.6289 | 1st Qu.: 61609 | 1st Qu.:1.002e+09 | 1st Qu.:4.474e+12 | 1st Qu.:1.122e+10 | 1st Qu.:0.3578 | 1st Qu.:0.0353 | 1st Qu.:0.0010 | 1st Qu.: 0.0027 | 1st Qu.:4.245e+06 | 1st Qu.:2.887e+12 | 1st Qu.:6.328e+12 | 1st Qu.:6.274e+09 | 1st Qu.:1.645e+09 | 1st Qu.:1.122e+11 | 1st Qu.: 0.1991 | 1st Qu.: 0.0072 | 1st Qu.: 0.014 | 1st Qu.: 0.0015 | 1st Qu.: 0.0004 | 1st Qu.: 0.0295 | 1st Qu.:0.0013 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.: 1279.76 | 1st Qu.:0.0653 | 1st Qu.:0.1865 | 1st Qu.:0.2938 | 1st Qu.: 4.912 | 1st Qu.: 3.223 | 1st Qu.: 2.4489 | 1st Qu.: 5.98 | 1st Qu.: 9.488 | 1st Qu.:1.0000 | 1st Qu.: 87509 | 1st Qu.:1.981e+09 | 1st Qu.:1.221e+13 | 1st Qu.:2.353e+10 | 1st Qu.:0.2974 | 1st Qu.:0.0255 | 1st Qu.:0.0006 | 1st Qu.: 0.0016 | 1st Qu.:6.300e+06 | 1st Qu.:6.696e+12 | 1st Qu.:1.390e+13 | 1st Qu.:1.204e+10 | 1st Qu.:2.229e+09 | 1st Qu.:2.418e+11 | 1st Qu.: 0.1256 | 1st Qu.: 0.0030 | 1st Qu.: 0.0053 | 1st Qu.: 0.0008 | 1st Qu.: 0.0001 | 1st Qu.: 0.0154 | 1st Qu.:0.0007 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0 | 1st Qu.: 1721 | 1st Qu.:0.0625 | 1st Qu.:0.1858 | 1st Qu.:0.2851 | 1st Qu.: 5.152 | 1st Qu.: 3.424 | 1st Qu.: 2.6169 | 1st Qu.: 60814 | 1st Qu.:9.935e+08 | 1st Qu.:4.544e+12 | 1st Qu.:8.991e+09 | 1st Qu.:0.3269 | 1st Qu.:0.0304 | 1st Qu.:0.0008 | 1st Qu.: 0.0017 | 1st Qu.:3.855e+06 | 1st Qu.:2.554e+12 | 1st Qu.:4.996e+12 | 1st Qu.:4.610e+09 | 1st Qu.:1.031e+09 | 1st Qu.:9.700e+10 | 1st Qu.: 0.1632 | 1st Qu.: 0.0050 | 1st Qu.: 0.009 | 1st Qu.: 0.0009 | 1st Qu.: 0.0002 | 1st Qu.: 0.0213 | 1st Qu.:0.0011 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.: 1345.6 | 1st Qu.:0.0602 | 1st Qu.:0.1798 | 1st Qu.:0.2815 | 1st Qu.: 4.652 | 1st Qu.: 3.168 | 1st Qu.: 2.4482 | 1st Qu.: 0.0 | 1st Qu.: 0.00 | 1st Qu.:0.0000 | 1st Qu.: 80446 | 1st Qu.:1.677e+09 | 1st Qu.:9.703e+12 | 1st Qu.:1.576e+10 | 1st Qu.:0.286 | 1st Qu.:0.024 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:5.225e+06 | 1st Qu.:4.915e+12 | 1st Qu.:9.243e+12 | 1st Qu.:7.840e+09 | 1st Qu.:1.139e+09 | 1st Qu.:1.751e+11 | 1st Qu.: 0.113 | 1st Qu.: 0.003 | 1st Qu.: 0.004 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.013 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1641 | 1st Qu.:0.058 | 1st Qu.:0.178 | 1st Qu.:0.276 | 1st Qu.: 4.844 | 1st Qu.: 3.364 | 1st Qu.: 2.599 | 1st Qu.: 60306 | 1st Qu.:9.973e+08 | 1st Qu.:4.631e+12 | 1st Qu.:6.872e+09 | 1st Qu.: 0.297 | 1st Qu.: 0.025 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:3.494e+06 | 1st Qu.:2.266e+12 | 1st Qu.:3.813e+12 | 1st Qu.:3.509e+09 | 1st Qu.:5.793e+08 | 1st Qu.:8.407e+10 | 1st Qu.: 0.131 | 1st Qu.: 0.003 | 1st Qu.: 0.01 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.016 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1421 | 1st Qu.:0.057 | 1st Qu.:0.173 | 1st Qu.:0.273 | 1st Qu.: 4.382 | 1st Qu.: 3.117 | 1st Qu.: 2.435 | 1st Qu.: 0.000 | 1st Qu.: 0.0 | 1st Qu.:0.0000 | 1st Qu.: 71079 | 1st Qu.:1.326e+09 | 1st Qu.:6.878e+12 | 1st Qu.:1.046e+10 | 1st Qu.:0.279 | 1st Qu.:0.024 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:4.357e+06 | 1st Qu.:3.416e+12 | 1st Qu.:6.267e+12 | 1st Qu.:4.954e+09 | 1st Qu.:6.858e+08 | 1st Qu.:1.308e+11 | 1st Qu.: 0.105 | 1st Qu.: 0.002 | 1st Qu.: 0.003 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.011 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1532 | 1st Qu.:0.055 | 1st Qu.:0.174 | 1st Qu.:0.267 | 1st Qu.: 4.606 | 1st Qu.: 3.294 | 1st Qu.: 2.562 | 1st Qu.: 59308 | 1st Qu.:9.687e+08 | 1st Qu.:4.582e+12 | 1st Qu.:5.444e+09 | 1st Qu.: 0.275 | 1st Qu.: 0.022 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:3.176e+06 | 1st Qu.:1.856e+12 | 1st Qu.:2.981e+12 | 1st Qu.:2.664e+09 | 1st Qu.:3.907e+08 | 1st Qu.:7.490e+10 | 1st Qu.: 0.111 | 1st Qu.: 0.002 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.012 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1439.6 | 1st Qu.:0.054 | 1st Qu.:0.168 | 1st Qu.:0.265 | 1st Qu.: 4.201 | 1st Qu.: 3.066 | 1st Qu.: 2.413 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.00 | 1st Qu.: 63638 | 1st Qu.:1.119e+09 | 1st Qu.:5.454e+12 | 1st Qu.:7.214e+09 | 1st Qu.:0.273 | 1st Qu.:0.023 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:3.620e+06 | 1st Qu.:2.433e+12 | 1st Qu.:4.051e+12 | 1st Qu.:3.319e+09 | 1st Qu.:3.608e+08 | 1st Qu.:9.492e+10 | 1st Qu.: 0.099 | 1st Qu.: 0.002 | 1st Qu.: 0.003 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.010 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1461 | 1st Qu.:0.054 | 1st Qu.:0.169 | 1st Qu.:0.273 | 1st Qu.: 4.408 | 1st Qu.: 3.226 | 1st Qu.: 2.530 | 1st Qu.: 58067 | 1st Qu.:9.551e+08 | 1st Qu.:4.472e+12 | 1st Qu.:4.431e+09 | 1st Qu.: 0.253 | 1st Qu.:0.019 | 1st Qu.:0.000 | 1st Qu.: 0.001 | 1st Qu.:2.854e+06 | 1st Qu.:1.644e+12 | 1st Qu.:2.343e+12 | 1st Qu.:2.080e+09 | 1st Qu.:2.396e+08 | 1st Qu.:6.389e+10 | 1st Qu.: 0.094 | 1st Qu.: 0.002 | 1st Qu.: 0.0 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.009 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1473.5 | 1st Qu.:0.053 | 1st Qu.:0.163 | 1st Qu.:0.271 | 1st Qu.: 4.032 | 1st Qu.: 3.000 | 1st Qu.: 2.393 | 1st Qu.: 0.00 | 1st Qu.: 0.00 | 1st Qu.:0.0000 | 1st Qu.: 57285 | 1st Qu.:9.262e+08 | 1st Qu.:4.274e+12 | 1st Qu.:4.645e+09 | 1st Qu.: 0.266 | 1st Qu.:0.022 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:2.981e+06 | 1st Qu.:1.764e+12 | 1st Qu.:2.516e+12 | 1st Qu.:2.130e+09 | 1st Qu.:2.076e+08 | 1st Qu.:6.809e+10 | 1st Qu.: 0.093 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.009 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1401 | 1st Qu.:0.056 | 1st Qu.:0.166 | 1st Qu.:0.287 | 1st Qu.: 4.190 | 1st Qu.: 3.164 | 1st Qu.: 2.517 | 1st Qu.: 56014 | 1st Qu.:9.081e+08 | 1st Qu.:4.089e+12 | 1st Qu.:3.213e+09 | 1st Qu.: 0.234 | 1st Qu.:0.017 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:2.507e+06 | 1st Qu.:1.371e+12 | 1st Qu.:1.719e+12 | 1st Qu.:1.521e+09 | 1st Qu.:1.409e+08 | 1st Qu.:5.238e+10 | 1st Qu.: 0.080 | 1st Qu.: 0.001 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.007 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.: 1485.3 | 1st Qu.:0.056 | 1st Qu.:0.162 | 1st Qu.:0.288 | 1st Qu.: 3.841 | 1st Qu.: 2.954 | 1st Qu.: 2.384 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 51008 | 1st Qu.:7.495e+08 | 1st Qu.:3.256e+12 | 1st Qu.:2.976e+09 | 1st Qu.:0.259 | 1st Qu.:0.021 | 1st Qu.:0.001 | 1st Qu.: 0.000 | 1st Qu.:2.419e+06 | 1st Qu.:1.272e+12 | 1st Qu.:1.559e+12 | 1st Qu.:1.351e+09 | 1st Qu.:1.110e+08 | 1st Qu.:4.633e+10 | 1st Qu.: 0.087 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.008 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1348 | 1st Qu.:0.061 | 1st Qu.:0.171 | 1st Qu.:0.321 | 1st Qu.: 3.990 | 1st Qu.: 3.089 | 1st Qu.: 2.522 | 1st Qu.: 53459 | 1st Qu.:8.456e+08 | 1st Qu.:3.990e+12 | 1st Qu.:2.255e+09 | 1st Qu.:0.220 | 1st Qu.:0.015 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:2.225e+06 | 1st Qu.:1.118e+12 | 1st Qu.:1.272e+12 | 1st Qu.:1.056e+09 | 1st Qu.:8.450e+07 | 1st Qu.:4.341e+10 | 1st Qu.: 0.069 | 1st Qu.: 0.001 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.006 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1511.6 | 1st Qu.:0.061 | 1st Qu.:0.165 | 1st Qu.:0.318 | 1st Qu.: 3.670 | 1st Qu.: 2.887 | 1st Qu.: 2.384 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 46145 | 1st Qu.:6.309e+08 | 1st Qu.:2.522e+12 | 1st Qu.:1.987e+09 | 1st Qu.:0.255 | 1st Qu.:0.021 | 1st Qu.:0.001 | 1st Qu.: 0.000 | 1st Qu.:2.023e+06 | 1st Qu.:9.101e+11 | 1st Qu.:1.081e+12 | 1st Qu.:8.812e+08 | 1st Qu.:6.662e+07 | 1st Qu.:3.486e+10 | 1st Qu.: 0.082 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.007 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1298 | 1st Qu.:0.071 | 1st Qu.:0.170 | 1st Qu.:0.371 | 1st Qu.: 3.840 | 1st Qu.: 3.024 | 1st Qu.: 2.518 | 1st Qu.: 52102 | 1st Qu.:8.038e+08 | 1st Qu.:3.678e+12 | 1st Qu.:1.671e+09 | 1st Qu.:0.211 | 1st Qu.:0.013 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:2.021e+06 | 1st Qu.:9.610e+11 | 1st Qu.:1.025e+12 | 1st Qu.:7.524e+08 | 1st Qu.:5.455e+07 | 1st Qu.:3.837e+10 | 1st Qu.: 0.062 | 1st Qu.: 0.001 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.005 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0 | 1st Qu.: 1531.5 | 1st Qu.:0.071 | 1st Qu.:0.162 | 1st Qu.:0.369 | 1st Qu.: 3.547 | 1st Qu.: 2.827 | 1st Qu.: 2.378 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 43744 | 1st Qu.:5.732e+08 | 1st Qu.:2.284e+12 | 1st Qu.:1.446e+09 | 1st Qu.:0.251 | 1st Qu.:0.020 | 1st Qu.:0.001 | 1st Qu.: 0.000 | 1st Qu.:1.885e+06 | 1st Qu.:8.044e+11 | 1st Qu.:8.956e+11 | 1st Qu.:6.424e+08 | 1st Qu.:4.822e+07 | 1st Qu.:3.039e+10 | 1st Qu.: 0.080 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.007 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0 | 1st Qu.: 1283 | 1st Qu.:0.083 | 1st Qu.:0.164 | 1st Qu.:0.415 | 1st Qu.: 3.771 | 1st Qu.: 2.979 | 1st Qu.: 2.513 | 1st Qu.: 50807 | 1st Qu.:7.782e+08 | 1st Qu.:3.589e+12 | 1st Qu.:1.327e+09 | 1st Qu.: 0.205 | 1st Qu.: 0.013 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:1.936e+06 | 1st Qu.:8.855e+11 | 1st Qu.:9.161e+11 | 1st Qu.:6.017e+08 | 1st Qu.:4.189e+07 | 1st Qu.:3.637e+10 | 1st Qu.: 0.058 | 1st Qu.: 0.001 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.004 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1565.5 | 1st Qu.:0.082 | 1st Qu.:0.156 | 1st Qu.:0.416 | 1st Qu.: 3.502 | 1st Qu.: 2.793 | 1st Qu.: 2.379 | 1st Qu.: 222.94 | 1st Qu.: 9.469 | 1st Qu.:0.02630 | 1st Qu.:0.1127 | 1st Qu.:0 | 1st Qu.: 0.819 | 1st Qu.:0.1811 | 1st Qu.:1.0000 | NA | NA | NA’s:14132 | 1st Qu.:0.2 | 1st Qu.:1.9 | 1st Qu.:1.4 | 1st Qu.:2 | 1st Qu.:1.800 | 1st Qu.:-7.560e-11 | 1st Qu.:0.209427 | 1st Qu.:0.1013822 | 1st Qu.:-1.1336 | 1st Qu.: 1.7394 | 1st Qu.:-6.600e-11 | 1st Qu.:0.20189 | 1st Qu.:0.103773 | 1st Qu.:-1.0312 | 1st Qu.: 1.6707 | 1st Qu.:-1.104e-10 | 1st Qu.:0.099798 | 1st Qu.:0.0220144 | 1st Qu.: -0.89931 | 1st Qu.: 1.29550 | 1st Qu.:-3.450e-11 | 1st Qu.:0.188691 | 1st Qu.:0.094683 | 1st Qu.:-0.9041 | 1st Qu.: 1.5904 | 1st Qu.: 303069 | 1st Qu.: 316088 | 1st Qu.: 522748 | 1st Qu.:0.2312 | 1st Qu.:-0.00873 | 1st Qu.:0.08642 | 1st Qu.:-0.1686900 | 1st Qu.:0.16439 | 1st Qu.:7.875e-02 | 1st Qu.:0.28159 | 1st Qu.:-0.00762 | 1st Qu.:0.05653 | 1st Qu.:-0.15855 | 1st Qu.:0.14448 | 1st Qu.: 0.073746 | 1st Qu.:0.28148 | 1st Qu.:-0.00317 | 1st Qu.:0.08416 | 1st Qu.:-0.028594 | 1st Qu.:0.10356 | 1st Qu.: 0.00600 | 1st Qu.:0.11254 | 1st Qu.:-0.00675 | 1st Qu.:0.03232 | 1st Qu.:-0.143829 | 1st Qu.:0.12733 | 1st Qu.: 0.066065 | 1st Qu.:0.26695 | 1st Qu.:-0.33681 | 1st Qu.:0.12728 | 1st Qu.:0.000e+00 | 1st Qu.:0.000e+00 | 1st Qu.: 0.3844 | 1st Qu.:-0.00788 | 1st Qu.:0.08475 | 1st Qu.:2.5 | 1st Qu.:7.1 | 1st Qu.:0.5 | |
| 115l BME 901 A: 1 | 3abk : 14 | GOL : 778 | 501 : 417 | C : 795 | Median : NA | Median : NA | Median : NA | Median : NA | Median : NA | Median : NA | Median : NA | Median : NA | Median : NA | Median : NA | Median : NA | Median : 7.00 | Median : 7.00 | Median : 7.00 | Median : 55.0 | Median : 52.00 | Median : 3.000 | Median : 0.00 | Median : 3.000 | Median :0.0000 | Median : 7.00 | Median : 56.0 | Median : 3.00 | Median : 0.000 | Median : 3.000 | Median :0.0000 | Median : 18.614 | Median : 27.30 | Median :1.0000 | Median : 392324 | Median :3.374e+10 | Median :7.154e+14 | Median :1.747e+12 | Median :0.4826 | Median :0.0508 | Median :0.0012 | Median :0.0129 | Median :6.683e+07 | Median :5.855e+14 | Median :2.106e+15 | Median :1.004e+12 | Median :3.677e+11 | Median :1.259e+13 | Median : 0.3602 | Median : 0.0167 | Median : 0.0633 | Median :0.0074 | Median :0.0026 | Median : 0.0873 | Median :0.0014 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0e+00 | Median :0 | Median : 3580 | Median :0.1328 | Median :0.3432 | Median :0.5061 | Median : 8.634 | Median : 4.749 | Median : 3.275 | Median : 201961 | Median :8.933e+09 | Median :1.036e+14 | Median :4.992e+11 | Median :0.6608 | Median :0.0950 | Median :0.0030 | Median : 0.0331 | Median :3.292e+07 | Median :1.356e+14 | Median :5.501e+14 | Median :3.324e+11 | Median :1.688e+11 | Median :3.315e+12 | Median : 0.7094 | Median : 0.0617 | Median : 0.2383 | Median : 0.0212 | Median : 0.0092 | Median : 0.2346 | Median :0.0035 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median : 2421 | Median :0.1277 | Median :0.3387 | Median :0.5086 | Median : 8.278 | Median : 4.407 | Median : 3.0316 | Median : 17.561 | Median : 24.51 | Median :1.0000 | Median : 371987 | Median :3.003e+10 | Median :5.893e+14 | Median :1.510e+12 | Median :0.4898 | Median :0.0516 | Median :0.0012 | Median :0.0133 | Median :6.310e+07 | Median :4.979e+14 | Median :1.913e+15 | Median :8.845e+11 | Median :3.127e+11 | Median :1.150e+13 | Median : 0.3698 | Median : 0.0172 | Median : 0.0669 | Median :0.0077 | Median :0.0028 | Median : 0.0905 | Median :0.0014 | Median :0.0000 | Median :0.000 | Median :0.0000 | Median :0.000 | Median :0 | Median : 3438 | Median :0.1310 | Median :0.3406 | Median :0.5083 | Median : 8.615 | Median : 4.687 | Median : 3.2292 | Median : 202072 | Median :8.784e+09 | Median :9.831e+13 | Median :4.894e+11 | Median :0.6567 | Median :0.0935 | Median :0.0029 | Median : 0.0320 | Median :3.248e+07 | Median :1.308e+14 | Median :5.311e+14 | Median :3.206e+11 | Median :1.575e+11 | Median :3.324e+12 | Median : 0.6992 | Median : 0.0591 | Median : 0.234 | Median : 0.0207 | Median : 0.0089 | Median : 0.2290 | Median :0.0035 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median : 2417.00 | Median :0.1259 | Median :0.3374 | Median :0.5097 | Median : 8.306 | Median : 4.348 | Median : 2.9975 | Median : 14.92 | Median : 18.472 | Median :1.0000 | Median : 353222 | Median :2.632e+10 | Median :4.586e+14 | Median :1.271e+12 | Median :0.5084 | Median :0.0541 | Median :0.0013 | Median : 0.0147 | Median :5.957e+07 | Median :4.354e+14 | Median :1.765e+15 | Median :7.309e+11 | Median :2.667e+11 | Median :1.056e+13 | Median : 0.3991 | Median : 0.0192 | Median : 0.0794 | Median : 0.0086 | Median : 0.0031 | Median : 0.1038 | Median :0.0016 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0 | Median : 3199 | Median :0.1240 | Median :0.3398 | Median :0.5108 | Median : 8.686 | Median : 4.593 | Median : 3.1510 | Median : 217227 | Median :9.850e+09 | Median :1.054e+14 | Median :5.136e+11 | Median :0.6472 | Median :0.0889 | Median :0.0027 | Median : 0.0303 | Median :3.677e+07 | Median :1.554e+14 | Median :7.150e+14 | Median :3.379e+11 | Median :1.608e+11 | Median :4.074e+12 | Median : 0.6657 | Median : 0.0525 | Median : 0.219 | Median : 0.0196 | Median : 0.0080 | Median : 0.2166 | Median :0.0034 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median : 2471.8 | Median :0.1191 | Median :0.3397 | Median :0.5135 | Median : 8.412 | Median : 4.310 | Median : 2.9366 | Median : 12.2 | Median : 13.93 | Median :1.0000 | Median : 332182 | Median :2.274e+10 | Median :3.814e+14 | Median :1.155e+12 | Median :0.523 | Median :0.056 | Median :0.001 | Median : 0.016 | Median :5.614e+07 | Median :3.576e+14 | Median :1.544e+15 | Median :6.599e+11 | Median :2.269e+11 | Median :9.433e+12 | Median : 0.420 | Median : 0.021 | Median : 0.089 | Median : 0.009 | Median : 0.003 | Median : 0.114 | Median :0.002 | Median :0.000 | Median :0.00 | Median :0.000 | Median :0.000 | Median :0 | Median : 2992 | Median :0.121 | Median :0.346 | Median :0.518 | Median : 8.678 | Median : 4.500 | Median : 3.088 | Median : 228546 | Median :1.021e+10 | Median :1.089e+14 | Median :5.777e+11 | Median : 0.628 | Median : 0.084 | Median :0.002 | Median : 0.028 | Median :3.763e+07 | Median :1.593e+14 | Median :7.299e+14 | Median :3.627e+11 | Median :1.751e+11 | Median :4.446e+12 | Median : 0.630 | Median : 0.047 | Median : 0.20 | Median : 0.018 | Median : 0.008 | Median : 0.200 | Median :0.003 | Median :0.000 | Median : 0.000 | Median :0.00 | Median :0.000 | Median :0.000 | Median : 2517 | Median :0.117 | Median :0.347 | Median :0.521 | Median : 8.473 | Median : 4.238 | Median : 2.885 | Median : 9.215 | Median : 10.6 | Median :1.0000 | Median : 280935 | Median :1.629e+10 | Median :2.224e+14 | Median :7.137e+11 | Median :0.525 | Median :0.056 | Median :0.001 | Median : 0.015 | Median :4.370e+07 | Median :2.246e+14 | Median :9.829e+14 | Median :4.405e+11 | Median :1.563e+11 | Median :6.147e+12 | Median : 0.429 | Median : 0.021 | Median : 0.091 | Median : 0.009 | Median : 0.003 | Median : 0.116 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2709 | Median :0.119 | Median :0.350 | Median :0.527 | Median : 8.460 | Median : 4.360 | Median : 3.012 | Median : 208895 | Median :8.384e+09 | Median :8.358e+13 | Median :4.365e+11 | Median : 0.594 | Median : 0.076 | Median :0.002 | Median : 0.024 | Median :3.302e+07 | Median :1.173e+14 | Median :6.077e+14 | Median :2.828e+11 | Median :1.362e+11 | Median :3.757e+12 | Median : 0.569 | Median : 0.040 | Median : 0.16 | Median : 0.015 | Median : 0.006 | Median : 0.172 | Median :0.003 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median : 2479.6 | Median :0.114 | Median :0.348 | Median :0.533 | Median : 8.245 | Median : 4.104 | Median : 2.825 | Median : 3.495 | Median : 8.224 | Median :1.00 | Median : 235095 | Median :1.149e+10 | Median :1.206e+14 | Median :4.385e+11 | Median :0.516 | Median :0.053 | Median :0.001 | Median : 0.014 | Median :3.362e+07 | Median :1.342e+14 | Median :5.714e+14 | Median :2.675e+11 | Median :9.867e+10 | Median :4.056e+12 | Median : 0.406 | Median : 0.019 | Median : 0.081 | Median : 0.008 | Median : 0.003 | Median : 0.108 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2462 | Median :0.121 | Median :0.350 | Median :0.548 | Median : 8.105 | Median : 4.177 | Median : 2.942 | Median : 191922 | Median :7.194e+09 | Median :6.487e+13 | Median :3.234e+11 | Median : 0.545 | Median :0.066 | Median :0.002 | Median : 0.017 | Median :2.862e+07 | Median :8.374e+13 | Median :4.300e+14 | Median :2.165e+11 | Median :1.037e+11 | Median :3.022e+12 | Median : 0.481 | Median : 0.030 | Median : 0.1 | Median : 0.011 | Median : 0.005 | Median : 0.129 | Median :0.003 | Median :0.000 | Median : 0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median : 2418.0 | Median :0.117 | Median :0.351 | Median :0.556 | Median : 7.926 | Median : 3.941 | Median : 2.760 | Median : 0.00 | Median : 0.00 | Median :0.0000 | Median : 185677 | Median :7.099e+09 | Median :6.726e+13 | Median :2.095e+11 | Median : 0.485 | Median :0.048 | Median :0.001 | Median : 0.010 | Median :2.342e+07 | Median :6.457e+13 | Median :2.739e+14 | Median :1.217e+11 | Median :4.539e+10 | Median :2.319e+12 | Median : 0.353 | Median : 0.014 | Median : 0.061 | Median : 0.006 | Median : 0.002 | Median : 0.090 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0.000 | Median :0 | Median : 2232 | Median :0.129 | Median :0.370 | Median :0.585 | Median : 7.658 | Median : 3.950 | Median : 2.899 | Median : 165427 | Median :5.518e+09 | Median :4.585e+13 | Median :1.829e+11 | Median : 0.475 | Median :0.051 | Median :0.001 | Median : 0.009 | Median :2.158e+07 | Median :4.817e+13 | Median :2.463e+14 | Median :1.245e+11 | Median :5.890e+10 | Median :1.962e+12 | Median : 0.357 | Median : 0.017 | Median : 0.06 | Median : 0.006 | Median : 0.003 | Median : 0.085 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0.000 | Median : 2389.9 | Median :0.126 | Median :0.372 | Median :0.594 | Median : 7.482 | Median : 3.700 | Median : 2.717 | Median : 0.000 | Median : 0.000 | Median :0.0000 | Median : 150813 | Median :4.832e+09 | Median :3.949e+13 | Median :8.898e+10 | Median :0.441 | Median :0.042 | Median :0.001 | Median : 0.006 | Median :1.700e+07 | Median :3.448e+13 | Median :1.447e+14 | Median :5.238e+10 | Median :2.114e+10 | Median :1.353e+12 | Median : 0.298 | Median : 0.010 | Median : 0.043 | Median : 0.003 | Median : 0.001 | Median : 0.067 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2070 | Median :0.145 | Median :0.394 | Median :0.637 | Median : 7.263 | Median : 3.706 | Median : 2.857 | Median : 144521 | Median :4.340e+09 | Median :3.527e+13 | Median :9.948e+10 | Median :0.414 | Median :0.040 | Median :0.001 | Median : 0.004 | Median :1.634e+07 | Median :2.840e+13 | Median :1.462e+14 | Median :6.826e+10 | Median :3.280e+10 | Median :1.242e+12 | Median : 0.255 | Median : 0.009 | Median : 0.029 | Median : 0.003 | Median : 0.001 | Median : 0.052 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2363.1 | Median :0.140 | Median :0.400 | Median :0.654 | Median : 7.036 | Median : 3.471 | Median : 2.681 | Median : 0.000 | Median : 0.000 | Median :0.0000 | Median : 127679 | Median :3.534e+09 | Median :2.584e+13 | Median :4.158e+10 | Median :0.407 | Median :0.036 | Median :0.001 | Median : 0.003 | Median :1.275e+07 | Median :1.843e+13 | Median :8.248e+13 | Median :2.365e+10 | Median :9.899e+09 | Median :8.806e+11 | Median : 0.254 | Median : 0.008 | Median : 0.031 | Median : 0.002 | Median : 0.001 | Median : 0.052 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 1916 | Median :0.158 | Median :0.414 | Median :0.684 | Median : 6.899 | Median : 3.489 | Median : 2.817 | Median : 127405 | Median :3.506e+09 | Median :2.632e+13 | Median :5.500e+10 | Median :0.371 | Median :0.030 | Median :0.001 | Median : 0.002 | Median :1.319e+07 | Median :1.688e+13 | Median :9.255e+13 | Median :3.616e+10 | Median :1.775e+10 | Median :8.919e+11 | Median : 0.198 | Median : 0.005 | Median : 0.017 | Median : 0.001 | Median : 0.001 | Median : 0.037 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0 | Median : 2328.1 | Median :0.156 | Median :0.421 | Median :0.700 | Median : 6.616 | Median : 3.275 | Median : 2.650 | Median : 0.000 | Median : 0.000 | Median :0.0000 | Median : 116713 | Median :2.822e+09 | Median :1.990e+13 | Median :2.428e+10 | Median :0.392 | Median :0.034 | Median :0.001 | Median : 0.002 | Median :1.118e+07 | Median :1.360e+13 | Median :6.380e+13 | Median :1.480e+10 | Median :5.684e+09 | Median :7.004e+11 | Median : 0.234 | Median : 0.006 | Median : 0.027 | Median : 0.001 | Median : 0.000 | Median : 0.046 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0 | Median : 1847 | Median :0.167 | Median :0.413 | Median :0.722 | Median : 6.706 | Median : 3.360 | Median : 2.773 | Median : 124612 | Median :3.209e+09 | Median :2.297e+13 | Median :3.406e+10 | Median : 0.347 | Median : 0.026 | Median :0.001 | Median : 0.001 | Median :1.204e+07 | Median :1.361e+13 | Median :7.942e+13 | Median :2.196e+10 | Median :1.126e+10 | Median :7.923e+11 | Median : 0.175 | Median : 0.004 | Median : 0.014 | Median : 0.001 | Median : 0.000 | Median : 0.030 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median : 2294.0 | Median :0.166 | Median :0.422 | Median :0.734 | Median : 6.407 | Median : 3.166 | Median : 2.618 | Median : 414.56 | Median : 18.614 | Median :0.03704 | Median :0.1532 | Median :0 | Median : 1.281 | Median :0.2529 | Median :1.0000 | NA | NA | NA | Median :0.2 | Median :1.9 | Median :1.4 | Median :2 | Median :2.040 | Median : 1.046e-10 | Median :0.273372 | Median :0.1720145 | Median :-0.9347 | Median : 2.5344 | Median : 1.430e-10 | Median :0.26292 | Median :0.177083 | Median :-0.8379 | Median : 2.4168 | Median : 6.200e-12 | Median :0.130447 | Median :0.0413683 | Median : -0.71010 | Median : 1.93808 | Median : 1.402e-10 | Median :0.245466 | Median :0.163666 | Median :-0.7254 | Median : 2.3201 | Median : 717445 | Median : 514708 | Median : 885746 | Median :0.3320 | Median :-0.00457 | Median :0.11254 | Median :-0.1453470 | Median :0.19674 | Median :8.654e-02 | Median :0.35584 | Median :-0.00383 | Median :0.07331 | Median :-0.13823 | Median :0.17009 | Median : 0.082145 | Median :0.35579 | Median :-0.00132 | Median :0.10912 | Median :-0.018357 | Median :0.13054 | Median : 0.01149 | Median :0.14498 | Median :-0.00305 | Median :0.04169 | Median :-0.126363 | Median :0.15054 | Median : 0.074527 | Median :0.33948 | Median :-0.30237 | Median :0.14707 | Median :0.000e+00 | Median :0.000e+00 | Median : 0.4401 | Median :-0.00367 | Median :0.11066 | Median :2.5 | Median :7.1 | Median :0.5 | |
| 115l CL 173 A : 1 | 2afh : 11 | CA : 661 | 401 : 395 | D : 552 | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean :NaN | Mean : 13.59 | Mean : 13.43 | Mean : 13.05 | Mean :101.5 | Mean : 98.33 | Mean : 7.332 | Mean : 1.35 | Mean : 3.817 | Mean :0.2208 | Mean : 13.74 | Mean :103.8 | Mean : 7.47 | Mean : 1.357 | Mean : 3.979 | Mean :0.2213 | Mean : 31.535 | Mean : 51.45 | Mean :0.9725 | Mean : 3091661 | Mean :1.622e+13 | Mean :1.938e+20 | Mean :2.123e+16 | Mean :0.5752 | Mean :0.0782 | Mean :0.0026 | Mean :0.0651 | Mean :4.023e+09 | Mean :1.143e+20 | Mean :6.415e+20 | Mean :1.253e+16 | Mean :6.720e+15 | Mean :1.592e+17 | Mean : 0.6958 | Mean : 0.1159 | Mean : 1.0010 | Mean :0.0416 | Mean :0.0260 | Mean : 0.4266 | Mean :0.0022 | Mean :0.0000 | Mean :0.0000 | Mean :0.0000 | Mean :0e+00 | Mean :0 | Mean : 6622 | Mean :0.2174 | Mean :0.4061 | Mean :0.5133 | Mean :10.851 | Mean : 5.807 | Mean : 3.682 | Mean : 1670086 | Mean :3.188e+12 | Mean :4.092e+18 | Mean :3.482e+15 | Mean :0.7665 | Mean :0.1466 | Mean :0.0071 | Mean : 0.2521 | Mean :2.036e+09 | Mean :1.025e+19 | Mean :7.811e+19 | Mean :2.193e+15 | Mean :1.334e+15 | Mean :2.463e+16 | Mean : 1.4490 | Mean : 0.6412 | Mean : 6.6229 | Mean : 0.1849 | Mean : 0.1401 | Mean : 1.4628 | Mean :0.0078 | Mean :0.0000 | Mean :0.0004 | Mean :0.0001 | Mean :0.0001 | Mean :0.0000 | Mean : 4059 | Mean :0.2206 | Mean :0.4058 | Mean :0.5151 | Mean :10.400 | Mean : 5.487 | Mean : 3.4481 | Mean : 29.751 | Mean : 46.67 | Mean :0.9522 | Mean : 2894498 | Mean :1.384e+13 | Mean :1.558e+20 | Mean :1.829e+16 | Mean :0.5905 | Mean :0.0823 | Mean :0.0028 | Mean :0.0756 | Mean :3.739e+09 | Mean :9.661e+19 | Mean :5.336e+20 | Mean :1.094e+16 | Mean :6.034e+15 | Mean :1.347e+17 | Mean : 0.7492 | Mean : 0.1351 | Mean : 1.2288 | Mean :0.0491 | Mean :0.0315 | Mean : 0.4913 | Mean :0.0024 | Mean :0.0000 | Mean :0.000 | Mean :0.0000 | Mean :0.000 | Mean :0 | Mean : 6285 | Mean :0.2203 | Mean :0.4073 | Mean :0.5139 | Mean :10.825 | Mean : 5.750 | Mean : 3.6288 | Mean : 1627744 | Mean :2.936e+12 | Mean :3.562e+18 | Mean :3.237e+15 | Mean :0.7698 | Mean :0.1473 | Mean :0.0071 | Mean : 0.2734 | Mean :1.973e+09 | Mean :9.264e+18 | Mean :7.123e+19 | Mean :2.063e+15 | Mean :1.281e+15 | Mean :2.274e+16 | Mean : 1.4893 | Mean : 0.6688 | Mean : 7.645 | Mean : 0.2013 | Mean : 0.1533 | Mean : 1.5884 | Mean :0.0079 | Mean :0.0000 | Mean :0.0004 | Mean :0.0001 | Mean :0.0001 | Mean :0.0000 | Mean : 4006.64 | Mean :0.2236 | Mean :0.4073 | Mean :0.5159 | Mean :10.396 | Mean : 5.447 | Mean : 3.4090 | Mean : 25.29 | Mean : 36.283 | Mean :0.8789 | Mean : 2498087 | Mean :9.441e+12 | Mean :9.121e+19 | Mean :1.266e+16 | Mean :0.6297 | Mean :0.0941 | Mean :0.0033 | Mean : 0.1094 | Mean :3.159e+09 | Mean :6.363e+19 | Mean :3.286e+20 | Mean :7.812e+15 | Mean :4.583e+15 | Mean :8.826e+16 | Mean : 0.8958 | Mean : 0.2128 | Mean : 2.0358 | Mean : 0.0736 | Mean : 0.0498 | Mean : 0.6871 | Mean :0.0030 | Mean :0.0000 | Mean :0.0000 | Mean :0.0000 | Mean :0.0000 | Mean :0 | Mean : 5623 | Mean :0.2274 | Mean :0.4111 | Mean :0.5145 | Mean :10.830 | Mean : 5.664 | Mean : 3.5406 | Mean : 1535388 | Mean :2.373e+12 | Mean :2.490e+18 | Mean :2.667e+15 | Mean :0.7767 | Mean :0.1502 | Mean :0.0071 | Mean : 0.3481 | Mean :1.830e+09 | Mean :7.042e+18 | Mean :5.556e+19 | Mean :1.749e+15 | Mean :1.137e+15 | Mean :1.846e+16 | Mean : 1.5867 | Mean : 0.8918 | Mean : 10.868 | Mean : 0.2686 | Mean : 0.2157 | Mean : 1.8945 | Mean :0.0081 | Mean :0.0000 | Mean :0.0005 | Mean :0.0001 | Mean :0.0001 | Mean :0.0000 | Mean : 3919.5 | Mean :0.2311 | Mean :0.4116 | Mean :0.5172 | Mean :10.432 | Mean : 5.387 | Mean : 3.3448 | Mean : 21.1 | Mean : 28.00 | Mean :0.7844 | Mean : 2143093 | Mean :6.474e+12 | Mean :5.200e+19 | Mean :9.143e+15 | Mean :0.664 | Mean :0.105 | Mean :0.004 | Mean : 0.147 | Mean :2.643e+09 | Mean :4.120e+19 | Mean :2.049e+20 | Mean :5.802e+15 | Mean :3.575e+15 | Mean :5.861e+16 | Mean : 1.043 | Mean : 0.320 | Mean : 3.088 | Mean : 0.100 | Mean : 0.068 | Mean : 0.928 | Mean :0.004 | Mean :0.000 | Mean :0.00 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 5042 | Mean :0.236 | Mean :0.417 | Mean :0.518 | Mean :10.758 | Mean : 5.557 | Mean : 3.458 | Mean : 1416666 | Mean :1.856e+12 | Mean :1.666e+18 | Mean :2.191e+15 | Mean : 0.780 | Mean : 0.153 | Mean :0.007 | Mean : 0.434 | Mean :1.652e+09 | Mean :5.106e+18 | Mean :4.232e+19 | Mean :1.487e+15 | Mean :1.019e+15 | Mean :1.456e+16 | Mean : 1.669 | Mean : 1.234 | Mean : 19.76 | Mean : 0.334 | Mean : 0.268 | Mean : 2.430 | Mean :0.009 | Mean :0.000 | Mean : 0.002 | Mean :0.00 | Mean :0.000 | Mean :0.000 | Mean : 3800 | Mean :0.240 | Mean :0.418 | Mean :0.522 | Mean :10.377 | Mean : 5.298 | Mean : 3.279 | Mean : 17.299 | Mean : 21.4 | Mean :0.6916 | Mean : 1793267 | Mean :4.402e+12 | Mean :2.929e+19 | Mean :6.519e+15 | Mean :0.689 | Mean :0.113 | Mean :0.004 | Mean : 0.191 | Mean :2.154e+09 | Mean :2.754e+19 | Mean :1.289e+20 | Mean :4.253e+15 | Mean :2.743e+15 | Mean :3.912e+16 | Mean : 1.176 | Mean : 0.461 | Mean : 5.020 | Mean : 0.136 | Mean : 0.099 | Mean : 1.241 | Mean :0.004 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 4477 | Mean :0.242 | Mean :0.422 | Mean :0.522 | Mean :10.534 | Mean : 5.386 | Mean : 3.353 | Mean : 1256622 | Mean :1.367e+12 | Mean :1.039e+18 | Mean :1.695e+15 | Mean : 0.771 | Mean : 0.148 | Mean :0.007 | Mean : 0.541 | Mean :1.425e+09 | Mean :3.628e+18 | Mean :3.046e+19 | Mean :1.206e+15 | Mean :8.804e+14 | Mean :1.084e+16 | Mean : 1.688 | Mean : 1.227 | Mean : 25.74 | Mean : 0.456 | Mean : 0.400 | Mean : 2.607 | Mean :0.008 | Mean :0.000 | Mean :0.001 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean : 3618.9 | Mean :0.246 | Mean :0.423 | Mean :0.526 | Mean :10.167 | Mean : 5.141 | Mean : 3.188 | Mean : 13.862 | Mean : 16.100 | Mean :0.59 | Mean : 1489327 | Mean :2.966e+12 | Mean :1.528e+19 | Mean :4.724e+15 | Mean :0.699 | Mean :0.116 | Mean :0.004 | Mean : 0.253 | Mean :1.726e+09 | Mean :1.838e+19 | Mean :8.501e+19 | Mean :3.188e+15 | Mean :2.164e+15 | Mean :2.655e+16 | Mean : 1.268 | Mean : 0.495 | Mean : 7.081 | Mean : 0.193 | Mean : 0.153 | Mean : 1.495 | Mean :0.005 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 4002 | Mean :0.251 | Mean :0.427 | Mean :0.532 | Mean :10.218 | Mean : 5.179 | Mean : 3.254 | Mean : 1092621 | Mean :9.870e+11 | Mean :6.227e+17 | Mean :1.339e+15 | Mean : 0.753 | Mean :0.141 | Mean :0.006 | Mean : 1.045 | Mean :1.196e+09 | Mean :2.495e+18 | Mean :2.148e+19 | Mean :9.911e+14 | Mean :7.593e+14 | Mean :7.930e+15 | Mean : 1.737 | Mean : 0.956 | Mean : 73.8 | Mean : 0.958 | Mean : 0.900 | Mean : 3.705 | Mean :0.009 | Mean :0.000 | Mean : 0.009 | Mean :0.001 | Mean :0.001 | Mean :0.000 | Mean : 3446.0 | Mean :0.256 | Mean :0.428 | Mean :0.537 | Mean : 9.854 | Mean : 4.944 | Mean : 3.097 | Mean : 10.88 | Mean : 11.94 | Mean :0.4852 | Mean : 1222842 | Mean :2.019e+12 | Mean :8.302e+18 | Mean :3.378e+15 | Mean : 0.687 | Mean :0.112 | Mean :0.004 | Mean : 0.278 | Mean :1.350e+09 | Mean :1.269e+19 | Mean :5.687e+19 | Mean :2.295e+15 | Mean :1.573e+15 | Mean :1.828e+16 | Mean : 1.299 | Mean : 1.250 | Mean : 12.227 | Mean : 0.224 | Mean : 0.187 | Mean : 1.879 | Mean :0.005 | Mean :0.000 | Mean :0.000 | Mean :0.00 | Mean :0.000 | Mean :0 | Mean : 3606 | Mean :0.266 | Mean :0.437 | Mean :0.550 | Mean : 9.751 | Mean : 4.941 | Mean : 3.170 | Mean : 926558 | Mean :6.708e+11 | Mean :3.182e+17 | Mean :9.803e+14 | Mean : 0.717 | Mean :0.129 | Mean :0.005 | Mean : 0.478 | Mean :9.550e+08 | Mean :1.601e+18 | Mean :1.355e+19 | Mean :7.529e+14 | Mean :6.013e+14 | Mean :5.339e+15 | Mean : 1.628 | Mean : 3.869 | Mean : 31.36 | Mean : 0.415 | Mean : 0.372 | Mean : 2.996 | Mean :0.008 | Mean :0.000 | Mean :0.002 | Mean :0.000 | Mean :0.00 | Mean :0.000 | Mean : 3285.4 | Mean :0.271 | Mean :0.439 | Mean :0.555 | Mean : 9.395 | Mean : 4.716 | Mean : 3.018 | Mean : 8.499 | Mean : 8.825 | Mean :0.3972 | Mean : 980603 | Mean :1.345e+12 | Mean :4.072e+18 | Mean :2.389e+15 | Mean :0.650 | Mean :0.103 | Mean :0.004 | Mean : 0.315 | Mean :1.037e+09 | Mean :8.459e+18 | Mean :3.897e+19 | Mean :1.649e+15 | Mean :1.156e+15 | Mean :1.262e+16 | Mean : 1.182 | Mean : 0.447 | Mean : 9.429 | Mean : 0.263 | Mean : 0.229 | Mean : 1.570 | Mean :0.005 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 3254 | Mean :0.285 | Mean :0.451 | Mean :0.577 | Mean : 9.133 | Mean : 4.684 | Mean : 3.096 | Mean : 761473 | Mean :4.467e+11 | Mean :1.706e+17 | Mean :6.972e+14 | Mean :0.659 | Mean :0.114 | Mean :0.005 | Mean : 0.406 | Mean :7.320e+08 | Mean :1.082e+18 | Mean :9.105e+18 | Mean :5.415e+14 | Mean :4.377e+14 | Mean :3.615e+15 | Mean : 1.407 | Mean : 0.689 | Mean : 11.904 | Mean : 0.343 | Mean : 0.300 | Mean : 2.001 | Mean :0.007 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 3133.6 | Mean :0.290 | Mean :0.453 | Mean :0.582 | Mean : 8.797 | Mean : 4.464 | Mean : 2.948 | Mean : 6.594 | Mean : 6.491 | Mean :0.3167 | Mean : 770168 | Mean :8.672e+11 | Mean :1.746e+18 | Mean :1.586e+15 | Mean :0.599 | Mean :0.088 | Mean :0.003 | Mean : 0.201 | Mean :7.658e+08 | Mean :5.362e+18 | Mean :2.466e+19 | Mean :1.111e+15 | Mean :7.948e+14 | Mean :8.283e+15 | Mean : 0.996 | Mean : 0.314 | Mean : 5.072 | Mean : 0.157 | Mean : 0.128 | Mean : 1.152 | Mean :0.004 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 2951 | Mean :0.302 | Mean :0.462 | Mean :0.604 | Mean : 8.469 | Mean : 4.391 | Mean : 3.013 | Mean : 607846 | Mean :2.916e+11 | Mean :8.687e+16 | Mean :4.451e+14 | Mean :0.596 | Mean :0.097 | Mean :0.004 | Mean : 0.321 | Mean :5.321e+08 | Mean :7.080e+17 | Mean :5.720e+18 | Mean :3.426e+14 | Mean :2.742e+14 | Mean :2.388e+15 | Mean : 1.205 | Mean : 0.627 | Mean : 8.923 | Mean : 0.262 | Mean : 0.223 | Mean : 1.636 | Mean :0.006 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.00 | Mean :0 | Mean : 2997.7 | Mean :0.306 | Mean :0.463 | Mean :0.610 | Mean : 8.159 | Mean : 4.179 | Mean : 2.866 | Mean : 5.125 | Mean : 4.805 | Mean :0.2491 | Mean : 615842 | Mean :5.264e+11 | Mean :5.428e+17 | Mean :1.288e+15 | Mean :0.564 | Mean :0.077 | Mean :0.003 | Mean : 0.197 | Mean :5.785e+08 | Mean :3.064e+18 | Mean :1.607e+19 | Mean :8.853e+14 | Mean :6.166e+14 | Mean :5.382e+15 | Mean : 0.908 | Mean : 0.327 | Mean : 6.447 | Mean : 0.167 | Mean : 0.147 | Mean : 1.216 | Mean :0.004 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.00 | Mean :0 | Mean : 2720 | Mean :0.314 | Mean :0.464 | Mean :0.631 | Mean : 7.959 | Mean : 4.151 | Mean : 2.954 | Mean : 498566 | Mean :1.960e+11 | Mean :4.007e+16 | Mean :3.507e+14 | Mean : 0.550 | Mean : 0.085 | Mean :0.004 | Mean : 0.349 | Mean :4.013e+08 | Mean :4.416e+17 | Mean :4.001e+18 | Mean :2.741e+14 | Mean :2.230e+14 | Mean :1.661e+15 | Mean : 1.116 | Mean : 1.051 | Mean : 14.992 | Mean : 0.297 | Mean : 0.262 | Mean : 1.927 | Mean :0.006 | Mean :0.000 | Mean :0.001 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean : 2900.9 | Mean :0.318 | Mean :0.465 | Mean :0.637 | Mean : 7.671 | Mean : 3.948 | Mean : 2.810 | Mean : 937.24 | Mean : 31.535 | Mean :0.04484 | Mean :0.1815 | Mean :0 | Mean : 1.690 | Mean :0.3039 | Mean :0.9725 | NA | NA | NA | Mean :0.2 | Mean :1.9 | Mean :1.4 | Mean :2 | Mean :2.128 | Mean :-7.000e-11 | Mean :0.275025 | Mean :0.1927164 | Mean :-0.9678 | Mean : 2.7854 | Mean : 2.942e-09 | Mean :0.26458 | Mean :0.197404 | Mean :-0.8724 | Mean : 2.5077 | Mean :-5.264e-10 | Mean :0.133948 | Mean :0.0575110 | Mean : -0.75040 | Mean : 2.38074 | Mean : 4.934e-10 | Mean :0.247163 | Mean :0.182585 | Mean :-0.7575 | Mean : 2.4045 | Mean : 1615555 | Mean : 745313 | Mean : 1369266 | Mean :0.3389 | Mean :-0.00593 | Mean :0.11494 | Mean :-0.1417209 | Mean :0.19472 | Mean :8.550e-02 | Mean :0.35322 | Mean :-0.00488 | Mean :0.07565 | Mean :-0.13486 | Mean :0.16813 | Mean : 0.081655 | Mean :0.35238 | Mean :-0.00190 | Mean :0.11144 | Mean :-0.018502 | Mean :0.13226 | Mean : 0.01071 | Mean :0.14801 | Mean :-0.00403 | Mean :0.04446 | Mean :-0.123219 | Mean :0.14792 | Mean : 0.074781 | Mean :0.33536 | Mean :-0.28451 | Mean :0.15577 | Mean :1.348e+73 | Mean :6.825e+71 | Mean : 0.4472 | Mean :-0.00524 | Mean :0.11325 | Mean :2.5 | Mean :7.1 | Mean :0.5 | |
| 123l BME 901 A: 1 | 3a0b : 11 | MG : 402 | 201 : 306 | E : 221 | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: NA | 3rd Qu.: 21.00 | 3rd Qu.: 20.00 | 3rd Qu.: 20.00 | 3rd Qu.:145.0 | 3rd Qu.:139.15 | 3rd Qu.:10.000 | 3rd Qu.: 2.00 | 3rd Qu.: 5.000 | 3rd Qu.:0.0000 | 3rd Qu.: 21.00 | 3rd Qu.:151.0 | 3rd Qu.:10.00 | 3rd Qu.: 2.000 | 3rd Qu.: 6.000 | 3rd Qu.:0.0000 | 3rd Qu.: 41.434 | 3rd Qu.: 67.06 | 3rd Qu.:1.0000 | 3rd Qu.: 2563883 | 3rd Qu.:1.258e+12 | 3rd Qu.:1.241e+17 | 3rd Qu.:2.287e+14 | 3rd Qu.:0.7365 | 3rd Qu.:0.0992 | 3rd Qu.:0.0028 | 3rd Qu.:0.0521 | 3rd Qu.:1.197e+09 | 3rd Qu.:1.535e+17 | 3rd Qu.:7.688e+17 | 3rd Qu.:1.300e+14 | 3rd Qu.:5.116e+13 | 3rd Qu.:1.647e+15 | 3rd Qu.: 0.8471 | 3rd Qu.: 0.0720 | 3rd Qu.: 0.4041 | 3rd Qu.:0.0306 | 3rd Qu.:0.0150 | 3rd Qu.: 0.3408 | 3rd Qu.:0.0026 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0e+00 | 3rd Qu.:0 | 3rd Qu.: 8604 | 3rd Qu.:0.2864 | 3rd Qu.:0.5983 | 3rd Qu.:0.7264 | 3rd Qu.:14.739 | 3rd Qu.: 7.444 | 3rd Qu.: 4.313 | 3rd Qu.: 1405891 | 3rd Qu.:3.835e+11 | 3rd Qu.:2.108e+16 | 3rd Qu.:7.100e+13 | 3rd Qu.:1.0132 | 3rd Qu.:0.1898 | 3rd Qu.:0.0074 | 3rd Qu.: 0.1622 | 3rd Qu.:6.281e+08 | 3rd Qu.:4.085e+16 | 3rd Qu.:2.124e+17 | 3rd Qu.:4.662e+13 | 3rd Qu.:2.294e+13 | 3rd Qu.:4.635e+14 | 3rd Qu.: 1.6894 | 3rd Qu.: 0.2874 | 3rd Qu.: 1.6383 | 3rd Qu.: 0.1056 | 3rd Qu.: 0.0611 | 3rd Qu.: 0.9441 | 3rd Qu.:0.0077 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.: 5322 | 3rd Qu.:0.3017 | 3rd Qu.:0.6051 | 3rd Qu.:0.7363 | 3rd Qu.:14.368 | 3rd Qu.: 7.143 | 3rd Qu.: 4.0015 | 3rd Qu.: 39.384 | 3rd Qu.: 61.21 | 3rd Qu.:1.0000 | 3rd Qu.: 2444647 | 3rd Qu.:1.136e+12 | 3rd Qu.:1.036e+17 | 3rd Qu.:2.211e+14 | 3rd Qu.:0.7633 | 3rd Qu.:0.1046 | 3rd Qu.:0.0029 | 3rd Qu.:0.0576 | 3rd Qu.:1.145e+09 | 3rd Qu.:1.387e+17 | 3rd Qu.:7.039e+17 | 3rd Qu.:1.258e+14 | 3rd Qu.:4.840e+13 | 3rd Qu.:1.513e+15 | 3rd Qu.: 0.9138 | 3rd Qu.: 0.0810 | 3rd Qu.: 0.4712 | 3rd Qu.:0.0344 | 3rd Qu.:0.0168 | 3rd Qu.: 0.3852 | 3rd Qu.:0.0029 | 3rd Qu.:0.0000 | 3rd Qu.:0.000 | 3rd Qu.:0.0000 | 3rd Qu.:0.000 | 3rd Qu.:0 | 3rd Qu.: 8213 | 3rd Qu.:0.2989 | 3rd Qu.:0.6085 | 3rd Qu.:0.7338 | 3rd Qu.:14.839 | 3rd Qu.: 7.393 | 3rd Qu.: 4.2305 | 3rd Qu.: 1411425 | 3rd Qu.:3.754e+11 | 3rd Qu.:1.981e+16 | 3rd Qu.:7.614e+13 | 3rd Qu.:1.0250 | 3rd Qu.:0.1896 | 3rd Qu.:0.0073 | 3rd Qu.: 0.1652 | 3rd Qu.:6.229e+08 | 3rd Qu.:4.167e+16 | 3rd Qu.:2.155e+17 | 3rd Qu.:4.987e+13 | 3rd Qu.:2.406e+13 | 3rd Qu.:4.685e+14 | 3rd Qu.: 1.7199 | 3rd Qu.: 0.2869 | 3rd Qu.: 1.696 | 3rd Qu.: 0.1089 | 3rd Qu.: 0.0639 | 3rd Qu.: 0.9822 | 3rd Qu.:0.0078 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.: 5277.11 | 3rd Qu.:0.3139 | 3rd Qu.:0.6152 | 3rd Qu.:0.7444 | 3rd Qu.:14.466 | 3rd Qu.: 7.099 | 3rd Qu.: 3.9472 | 3rd Qu.: 34.14 | 3rd Qu.: 47.706 | 3rd Qu.:1.0000 | 3rd Qu.: 2198054 | 3rd Qu.:9.254e+11 | 3rd Qu.:7.874e+16 | 3rd Qu.:1.902e+14 | 3rd Qu.:0.8274 | 3rd Qu.:0.1201 | 3rd Qu.:0.0033 | 3rd Qu.: 0.0759 | 3rd Qu.:1.030e+09 | 3rd Qu.:1.117e+17 | 3rd Qu.:5.691e+17 | 3rd Qu.:1.103e+14 | 3rd Qu.:4.242e+13 | 3rd Qu.:1.216e+15 | 3rd Qu.: 1.0766 | 3rd Qu.: 0.1077 | 3rd Qu.: 0.6657 | 3rd Qu.: 0.0462 | 3rd Qu.: 0.0222 | 3rd Qu.: 0.5005 | 3rd Qu.:0.0035 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0 | 3rd Qu.: 7378 | 3rd Qu.:0.3368 | 3rd Qu.:0.6307 | 3rd Qu.:0.7479 | 3rd Qu.:15.036 | 3rd Qu.: 7.290 | 3rd Qu.: 4.1043 | 3rd Qu.: 1379860 | 3rd Qu.:3.597e+11 | 3rd Qu.:1.752e+16 | 3rd Qu.:8.023e+13 | 3rd Qu.:1.0468 | 3rd Qu.:0.1909 | 3rd Qu.:0.0069 | 3rd Qu.: 0.1823 | 3rd Qu.:6.298e+08 | 3rd Qu.:3.960e+16 | 3rd Qu.:2.164e+17 | 3rd Qu.:5.226e+13 | 3rd Qu.:2.598e+13 | 3rd Qu.:4.794e+14 | 3rd Qu.: 1.7974 | 3rd Qu.: 0.3002 | 3rd Qu.: 1.901 | 3rd Qu.: 0.1243 | 3rd Qu.: 0.0725 | 3rd Qu.: 1.0608 | 3rd Qu.:0.0079 | 3rd 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| NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA | NA | NA | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA | NA | NA | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA | NA | NA | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA | NA | NA | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :139 | NA’s :139 | NA’s :139 | NA’s :139 | NA’s :139 | NA’s :77 | NA’s :77 | NA | NA | NA |
** Wnioski z analizy podsumowanych danych: **
NA),NA na 0dane <- dane %>%
replace(is.na(.), 0)
Po ogólnej analizie danych, można zauważyć, że pewne kolumny zawierają stałe wartości:
wartość 0 przyjmują: local_BAa, local_NPa, local_Ra, local_RGa, local_SRGa, local_CCSa, local_CCPa, local_ZOa, ocal_ZDa, local_ZD_minus_a, local_ZD_plus_a
Wartość ‘DELFWT’ przyjmuje: fo_col
Wartość ‘PHDELWT’ przyjmuje: fc_col
Wartość 0 przyjmuje: weight_col
Wartość 0.2 przyjmuje: grid_space
Wartość 1.9 przyjmuje: solvent_radius
Wartość 1.4 przyjmuje: solvent_opening_radius
Wartość 2 przyjmuje: resolution_max_limit
Wartość 2.5 przyjmuje: part_step_FoFc_std_min
Wartość 7.1 przyjmuje: part_step_FoFc_std_max
Wartość 0.5 przyjmuje: part_step_FoFc_std_step
Dlatego do dalszej analizy danych, nie bedziemy wykorzystywać wymienionych kolumn
dane <- dane %>%
select(-(local_BAa:local_ZD_plus_a), -(fo_col:resolution_max_limit), -(part_step_FoFc_std_min:part_step_FoFc_std_step))
W zbiorze danych znajdują się kolumny, których wartości są obliczane na podstawie całego pliku PDB, a nie tylko na podstawie ligandu:
TwoFoFc_mean, TwoFoFc_std, TwoFoFc_square_std, TwoFoFc_min, TwoFoFc_max
Fo_mean, Fo_std, Fo_square_std, Fo_min, Fo_max
FoFc_mean, FoFc_std, FoFc_square_std, FoFc_min, FoFc_max
Fc_mean, Fc_std, Fc_square_std, Fc_min, Fc_max
resolution
TwoFoFc_bulk_mean, TwoFoFc_bulk_std, TwoFoFc_void_mean, TwoFoFc_void_std, TwoFoFc_modeled_mean, TwoFoFc_modeled_std
Fo_bulk_mean, Fo_bulk_std, Fo_void_mean, Fo_void_std, Fo_modeled_mean, Fo_modeled_std
Fc_bulk_mean, Fc_bulk_std, Fc_void_mean, Fc_void_std, Fc_modeled_mean, Fc_modeled_std
FoFc_bulk_mean, FoFc_bulk_std, FoFc_void_mean, FoFc_void_std, FoFc_modeled_mean, FoFc_modeled_std
TwoFoFc_void_fit_binormal_mean1, TwoFoFc_void_fit_binormal_std1, TwoFoFc_void_fit_binormal_mean2, TwoFoFc_void_fit_binormal_std2, TwoFoFc_void_fit_binormal_scale, TwoFoFc_solvent_fit_normal_mean, TwoFoFc_solvent_fit_normal_std
Do dalszej analizy, nie bedziemy wykorzystywać kolumn wymienionych wyżej
dane <- dane %>%
select(-(TwoFoFc_mean:Fc_max), -resolution, -(TwoFoFc_bulk_mean:TwoFoFc_solvent_fit_normal_std))
Poniższe korelogramy reprezentują korelację między poszczególnymi zmiennymi. Zbiór wszystkich kolumn, dla analizowanych danych, jest bardzo duży, dlatego po wnikliwej analizie, można było zauważyć, że kolumny part_XX niosą tą samą informację, jednak dla innego progu odcięcia. Dlatego, aby ograniczyć zbiór porównywanych zmiennych, kolumny podzielone zostały na zbiór zmiennych ogólnych (nie zawierających kolumn part), a także na rozłączne zbiory part. Korelacja nie była liczona dla dwóch różnych zbiorów part (np. między part01 i part02), a jedynie wewnątrz poszczególnych zbiorów part i atrybutów ogólnych.
Filtrowanie zbioru danych i eliminacja kolumn zawierających wartości stałe i tekstowe ***
# Dane bez stałych wartości i wartości tekstowych
dane_obliczenia <- dane %>%
select(-(title:chain_id), -local_parts, -local_min, -part_00_blob_parts)
Są to wszystkie zmienne ogólne (nie zawierają kolumn part_XX_) ***
corrgram(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla podstawowych atrybutów",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_00 ***
# Dane dla part_00
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_00_blob_electron_sum:part_00_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_00",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_01 ***
# Dane dla part_01
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_01_blob_electron_sum:part_01_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_01",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_02 ***
# Dane dla part_02
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_02_blob_electron_sum:part_02_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_02",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_03 ***
# Dane dla part_03
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_03_blob_electron_sum:part_03_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_03",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_04 ***
# Dane dla part_04
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_04_blob_electron_sum:part_04_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_04",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_05 ***
# Dane dla part_05
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_05_blob_electron_sum:part_05_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_05",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_06 ***
# Dane dla part_06
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_06_blob_electron_sum:part_06_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_06",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_07 ***
# Dane dla part_07
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_07_blob_electron_sum:part_07_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_07",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_08 ***
# Dane dla part_08
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_08_blob_electron_sum:part_08_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_08",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
Są to wszystkie zmienne ogólne i kolumny z part_09 ***
# Dane dla part_09
corrgram(data.frame(dane_obliczenia %>%
select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)),
dane_obliczenia %>%
select(part_09_blob_electron_sum:part_09_density_sqrt_E3)),
order=TRUE,
main="Korelogram dla atrybutów wraz z part_09",
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.minmax,
text.panel=panel.txt)
res_namepodsumowanie <- dane %>%
group_by(res_name) %>%
summarise(n=n()) #Podliczenie
Liczba wszystkich klas res_name wynosi: ** 2685 **
Liczba ta jest bardzo duża, dlatego na poniższym wykresie przedstawione zostało 20 najliczniejszych klas.
podsumowanie <- podsumowanie %>%
arrange(desc(n)) %>%
slice(1:20)
podsumowanie$res_name <- factor(podsumowanie$res_name, levels=unique(podsumowanie$res_name))
ggplot(podsumowanie , aes(x = res_name, y = n, order = desc(n))) +
geom_bar(stat="identity") +
theme_bw()
rozk <- stack(dane %>% select(local_res_atom_non_h_count, local_res_atom_non_h_electron_sum))
ggplot(rozk, aes(x = values)) + geom_density(aes(group=ind, colour=ind, fill=ind), alpha=0.3)
Analizując powyższy wykres, można zauważyć, że:
local_res_atom_non_h_count wynosza odpowiednio 7 i 13.43local_res_atom_non_h_electron_sum są bardziej rozrzucone. Jednak analizując pierwszy (30) i trzeci (145) kwartyl widzimy, że wartości te są skoncetrowane bliżej średniej (101.5) i mediany (55), dlatego też na wykresie widać długi “ogon”d <- dane %>%
mutate(y = 6+(round(jitter(local_res_atom_non_h_count, factor = 2.5, amount = 0))), x = 50+(round(jitter(local_res_atom_non_h_electron_sum, factor = 3.5, amount = 0)))) %>% select(x, y)
rozklad <- ggplot(d,
aes(x = x,
y = y)) +
stat_density2d(geom="tile", aes(fill = ..density..), contour = FALSE) + #, position = position_jitter()) +
scale_fill_gradientn(colours=rev(brewer.pal(11, "Spectral"))) +
coord_cartesian(xlim = c(0,650),
ylim = c(0,100)) +
scale_x_continuous(breaks = seq(0,600,100)) +
scale_y_continuous(breaks = seq(0,100,20)) +
theme(legend.position="none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = rgb(0.368, 0.309, 0.635)))
#Wykresy margin
ggExtra::ggMarginal(
rozklad,
type = 'histogram',
margins = 'both',
size = 5,
xparams = list(binwidth = 6, colour = "black", fill = "red"),
yparams = list(binwidth = 1, colour = "black", fill = "red")
)
Do stworzenia wykresu, wykozystana została biblioteka ggplot2 i ggEkstra. Druga wymieniona biblioteka umożliwiła stowrzenie wykresów na poszczególnych osiach. Do pokolorowania rozkładu wykorzystana została biblioteka RColorBrewer, z której skorzystano z typu kolorów Spectral. Należało go jednak odwrócić. Aby dokładniej przeanalizowac dane, które były skupione blisko wartości zerowych, wykorzystany został jitter.
kable(dane %>%
mutate(blad = abs(dict_atom_non_h_count - local_res_atom_non_h_count)) %>%
select(res_name, blad) %>%
group_by(res_name) %>%
summarise(minimum = min(blad),
maximum = max(blad),
warjancja = var(blad),
odch_stand = sd(blad)) %>%
arrange(desc(maximum)) %>%
slice(1:10))
| res_name | minimum | maximum | warjancja | odch_stand |
|---|---|---|---|---|
| PEU | 64 | 69 | 12.5000 | 3.535534 |
| CDL | 0 | 60 | 527.6944 | 22.971601 |
| PC1 | 15 | 43 | 252.3333 | 15.885003 |
| 3TL | 0 | 33 | 363.0000 | 19.052559 |
| CPQ | 33 | 33 | NA | NaN |
| JEF | 33 | 33 | NA | NaN |
| PE3 | 0 | 33 | 544.5000 | 23.334524 |
| PEE | 2 | 33 | 267.3333 | 16.350331 |
| VV7 | 33 | 33 | NA | NaN |
| LI1 | 27 | 32 | 12.5000 | 3.535534 |
kable(dane %>%
mutate(blad = abs(dict_atom_non_h_electron_sum - local_res_atom_non_h_electron_sum)) %>%
select(res_name, blad) %>%
group_by(res_name) %>%
summarise(minimum = min(blad),
maximum = max(blad),
warjancja = var(blad),
odch_stand = sd(blad)) %>%
arrange(desc(maximum)) %>%
slice(1:10))
| res_name | minimum | maximum | warjancja | odch_stand |
|---|---|---|---|---|
| PEU | 426 | 460 | 578.00 | 24.04163 |
| CDL | 0 | 360 | 19013.11 | 137.88804 |
| PC1 | 91 | 284 | 11064.33 | 105.18713 |
| ATG | 252 | 252 | 0.00 | 0.00000 |
| SAP | 252 | 252 | NA | NaN |
| ABG | 242 | 242 | NA | NaN |
| CPQ | 225 | 225 | NA | NaN |
| PEE | 12 | 224 | 12761.33 | 112.96607 |
| VV7 | 224 | 224 | NA | NaN |
| PE3 | 0 | 222 | 24642.00 | 156.97771 |
part <- dane %>% select(part_01_blob_electron_sum:part_01_density_sqrt_E3)
for(i in seq_along(part)) {
srednia <- mean(part[,i], rm.na=TRUE)
min <- min(part[,i], rm.na=TRUE)
max <- max(part[,i], rm.na=TRUE)
w <- ggplot(data = part, aes(x=part[,i], label = srednia)) +
scale_x_continuous(colnames(part %>% select(i))) +
scale_y_continuous("Liczność") +
geom_histogram(aes(y = ..density.., fill = ..count..)) + geom_density(colour = "red", size = 1.5) +
scale_fill_gradientn(colours=brewer.pal(11, "Spectral")) +
geom_vline(data = NULL, aes(xintercept=srednia), linetype = "dashed", size=2) +
ggtitle(paste("Rozkład wartości dla atrybutu ", colnames(part %>% select(i)), " średnia=", srednia, sep = ""))
print(w)
}
Powyższe wykresy pokazują rozkład poszczególnych zmiennych dla Part_01. Patrząc na wcześniejsze podsumowanie surowych danych, można zauważyć, że kolumny te posiadały bardzo dużą liczbę wartości pustych, które w dalszej części zostały także zamienione na wartość 0 (stąd też widoczne wysokie rozkłady wartości w okolicach 0)
Do przewidywania liczby atomów wykorzystany został mogel regresji liniowej. Dodatkowo, porównany został sposób uczenia modelu regresji z wykorzystaniem standardowego próbkowania i walidacji krzyżowej. Analizując dalsze wyniki, oba podejścia nie zmieniły uzyskanych wyników. Do analizy atrybutów wykorzystane zostały wszystkie zmienna oprócz zmiennych słownikowych (dist_)
proba <- dane %>% select(-(title:chain_id), -(dict_atom_non_h_count:dict_atom_S_count))
inTraining <- createDataPartition(y=proba$local_res_atom_non_h_count,
p= .75,
list = FALSE)
training <- proba[ inTraining,]
testing <- proba[ -inTraining,]
"lm"lmFit <- train(local_res_atom_non_h_count ~ . ,
method="lm",
data = training)
"lm" z Cross-Validationctrl <- trainControl(method = "cv", number = 10)
lmCVFit <- train(local_res_atom_non_h_count ~ . ,
method="lm",
data = training,
trControl = ctrl)
"lm"plot(varImp(lmFit), top = 20)
"lm" z Cross-Validationplot(varImp(lmCVFit), top = 20)
"lm"predVal<-predict(lmFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_count, pred=predVal)
defaultSummary(modelvalues)
## RMSE Rsquared
## 1.4232369 0.9904637
"lm" z Cross-ValidationpredVal<-predict(lmCVFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_count, pred=predVal)
defaultSummary(modelvalues)
## RMSE Rsquared
## 1.4232369 0.9904637
"lm"predictedValues <- predict(lmFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_count, predictedValues)
"lm" z Cross-ValidationpredictedValues <- predict(lmCVFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_count, predictedValues)
proba <- dane %>% select(-(title:chain_id), -(dict_atom_non_h_count:dict_atom_S_count))
inTraining <- createDataPartition(y=proba$local_res_atom_non_h_electron_sum,
p= .75,
list = FALSE)
training <- proba[ inTraining,]
testing <- proba[ -inTraining,]
"lm"lmFit <- train(local_res_atom_non_h_electron_sum ~ . ,
method="lm",
data = training)
"lm" z Cross-Validationctrl <- trainControl(method = "cv", number = 10)
lmCVFit <- train(local_res_atom_non_h_electron_sum ~ . ,
method="lm",
data = training,
trControl = ctrl)
"lm"plot(varImp(lmFit), top = 20)
"lm" z Cross-Validationplot(varImp(lmCVFit), top = 20)
"lm"predVal<-predict(lmFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_electron_sum, pred=predVal)
defaultSummary(modelvalues)
## RMSE Rsquared
## 120.1233936 0.4140984
"lm" z Cross-ValidationpredVal<-predict(lmCVFit, testing)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
modelvalues<-data.frame(obs = testing$local_res_atom_non_h_electron_sum, pred=predVal)
defaultSummary(modelvalues)
## RMSE Rsquared
## 120.1233936 0.4140984
"lm"predictedValues <- predict(lmFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_electron_sum, predictedValues)
"lm" z Cross-ValidationpredictedValues <- predict(lmCVFit)
## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
plot(training$local_res_atom_non_h_electron_sum, predictedValues)
Do stworzenia klasyfikatora dla zmiennej res_name, wykorzystano wiedzę uzyskaną z powyższych badań. Znając dużą liczbę różnych wartości dla zmiennej res_name, klasyfikator został wykorzystany do zbudowania modelu dla wartości zmiennych przekraczających liczbę 50. Tym samym ograniczyło to liczbę analizowanego zbioru. Kolejnym wnioskiem wywinioskowanym z eksploracji danych było dostrzeżenie korelacji między zmiennymi. Powyższe korelogramy pokazały, że niektóre kolumny nie są ze sobą powiązane i tym samym niepotrzebne jest porównywanie ich do stworzenia modelu. Dlatego ustawiony został próg korelacji 0.90. Do budowy klasyfikacji wykorzystane zostały dwa modele - jeden z nich opierał się na algorytmie GBM, a drugi na Random Forest. Analizując poszczególne wyniki, algorytm Random Forest stworzył model, który okazał się bardziej trafnym (wyższy wskażnik Kappa i Accuracy). Analizując także Confussion Matrix, można dostrzec, że klasyfikator popełniał wysoki błąd dla niektórych ligardów.
da <- dane %>%
group_by(res_name) %>%
summarise(liczba = n()) %>%
filter(liczba > 50)
#res_name <- dane %>%
# filter(res_name %in% da$res_name) %>%
# select(res_name)
descr <- dane %>%
filter(res_name %in% da$res_name) %>%
select(-(res_id:dict_atom_S_count), -local_min, -title, -pdb_code)
cols = 2:length(descr)
descr[,cols] = apply(descr[,cols], 2, function(x) as.numeric(x))
#res_name <- res_name$res_name
predictors <- names(descr)[names(descr) != "res_name"]
inTrain <- createDataPartition(descr$res_name, p = 3/4, list = FALSE)
training <- descr[inTrain,]
testing <- descr[-inTrain,]
res_name_training <- training %>% select(res_name)
res_name_testing<- testing %>% select(res_name)
training <- training %>% select(-res_name)
testing <- testing %>% select(-res_name)
ncol(training)
## [1] 701
descrCorr <- cor(training)
highCorr <- findCorrelation(descrCorr, 0.90)
training <- training[, -highCorr]
testing <- testing[, -highCorr]
ncol(training)
## [1] 159
res_name_training <- res_name_training %>% mutate(name = paste("name_", res_name, sep="")) %>% select(name)
res_name_testing <- res_name_testing %>% mutate(name = paste("name_", res_name, sep="")) %>% select(name)
xTrans <- preProcess(training)
training <- predict(xTrans, training)
testing <- predict(xTrans, testing)
training <- data.frame(res_name_training, training)
testing <- data.frame(res_name_testing, testing)
gbmGrid <- expand.grid(interaction.depth = c(1, 5, 10),
n.trees = (5:10)*2,
shrinkage = 0.1,
n.minobsinnode = 20)
ctrl <- trainControl(method = "repeatedcv",
number = 2,
repeats = 5)
rfGrid <- expand.grid(mtry = c(10:15))
gbmFit <- train(name~.,
data = training,
method = "gbm",
trControl = ctrl,
verbose = FALSE,
bag.fraction = 0.5,
tuneGrid = gbmGrid)
rfFit <- train(name~.,
data = training,
method = "rf",
trControl = ctrl,
tuneGrid = rfGrid,
ntree = 30)
plot(gbmFit)
plot(gbmFit, metric = "Kappa")
plot(gbmFit, plotType = "level")
resampleHist(gbmFit)
plot(rfFit)
plot(rfFit, metric = "Kappa")
resampleHist(rfFit)
gbmPred <- predict(gbmFit, testing)
matrix <- as.matrix(confusionMatrix(gbmPred, testing$name))
kable(matrix, digits = 3, caption = "Confusion Matrix dla GBM")
| name_ACT | name_ADP | name_ATP | name_BME | name_CA | name_CD | name_CIT | name_CL | name_CO | name_CU | name_EDO | name_EPE | name_FAD | name_FE | name_FE2 | name_FMN | name_GDP | name_GOL | name_HEM | name_K | name_MAN | name_MES | name_MG | name_MN | name_MPD | name_NA | name_NAD | name_NAG | name_NAP | name_NDP | name_NI | name_PEG | name_PG4 | name_PLP | name_PO4 | name_SAH | name_SEP | name_SO4 | name_TRS | name_ZN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| name_ACT | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 6 | 0 | 2 |
| name_ADP | 0 | 12 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 |
| name_ATP | 0 | 5 | 8 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 |
| name_BME | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_CA | 0 | 1 | 4 | 2 | 82 | 0 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 6 | 2 | 0 | 0 | 8 | 1 | 3 | 1 | 1 | 11 | 8 | 0 | 3 | 1 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 6 | 0 | 1 | 11 | 1 | 15 |
| name_CD | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| name_CIT | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| name_CL | 6 | 0 | 0 | 0 | 4 | 1 | 0 | 52 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 11 | 0 | 0 | 4 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 0 | 0 | 15 | 0 | 3 |
| name_CO | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_CU | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 |
| name_EDO | 0 | 0 | 0 | 3 | 4 | 0 | 0 | 1 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 1 | 0 | 0 | 2 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 5 | 0 | 0 |
| name_EPE | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| name_FAD | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_FE | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
| name_FE2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| name_FMN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 10 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 1 |
| name_GDP | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_GOL | 9 | 3 | 0 | 4 | 7 | 0 | 5 | 6 | 1 | 0 | 38 | 2 | 1 | 1 | 0 | 0 | 0 | 119 | 1 | 1 | 5 | 3 | 12 | 0 | 10 | 16 | 1 | 13 | 0 | 1 | 1 | 11 | 3 | 0 | 4 | 0 | 1 | 11 | 6 | 3 |
| name_HEM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 62 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 3 |
| name_K | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| name_MAN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 1 |
| name_MES | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_MG | 0 | 0 | 2 | 1 | 17 | 1 | 1 | 3 | 0 | 0 | 4 | 1 | 1 | 0 | 1 | 0 | 1 | 7 | 0 | 2 | 4 | 0 | 32 | 4 | 0 | 8 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 8 | 2 | 10 |
| name_MN | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
| name_MPD | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_NA | 3 | 0 | 0 | 0 | 5 | 0 | 0 | 5 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 0 | 3 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| name_NAD | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_NAG | 0 | 2 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 8 | 1 | 0 | 4 | 1 | 7 | 0 | 0 | 1 | 1 | 62 | 2 | 1 | 0 | 0 | 5 | 0 | 1 | 1 | 1 | 2 | 2 | 0 |
| name_NAP | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 7 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_NDP | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_NI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_PEG | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_PG4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_PLP | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_PO4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 2 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 13 | 0 | 5 |
| name_SAH | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 |
| name_SEP | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 |
| name_SO4 | 6 | 2 | 0 | 0 | 24 | 2 | 1 | 19 | 2 | 1 | 4 | 3 | 0 | 3 | 0 | 0 | 0 | 11 | 0 | 4 | 0 | 6 | 14 | 1 | 0 | 9 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 37 | 0 | 1 | 212 | 1 | 16 |
| name_TRS | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| name_ZN | 1 | 0 | 1 | 0 | 8 | 8 | 0 | 0 | 7 | 14 | 0 | 0 | 0 | 11 | 7 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 2 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 1 | 7 | 0 | 1 | 7 | 0 | 134 |
rfPred <- predict(rfFit, testing)
matrix <- as.matrix(confusionMatrix(rfPred, testing$name))
kable(matrix, digits = 3, caption = "Confusion Matrix dla Random Forest")
| name_ACT | name_ADP | name_ATP | name_BME | name_CA | name_CD | name_CIT | name_CL | name_CO | name_CU | name_EDO | name_EPE | name_FAD | name_FE | name_FE2 | name_FMN | name_GDP | name_GOL | name_HEM | name_K | name_MAN | name_MES | name_MG | name_MN | name_MPD | name_NA | name_NAD | name_NAG | name_NAP | name_NDP | name_NI | name_PEG | name_PG4 | name_PLP | name_PO4 | name_SAH | name_SEP | name_SO4 | name_TRS | name_ZN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| name_ACT | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 4 | 0 | 0 |
| name_ADP | 0 | 11 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 9 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 4 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| name_ATP | 0 | 7 | 8 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 |
| name_BME | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_CA | 1 | 1 | 2 | 1 | 80 | 1 | 1 | 3 | 0 | 5 | 0 | 1 | 0 | 7 | 2 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 7 | 8 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 4 | 0 | 1 | 7 | 0 | 16 |
| name_CD | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_CIT | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| name_CL | 7 | 0 | 0 | 0 | 6 | 1 | 0 | 47 | 1 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 0 | 12 | 2 | 0 | 3 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 14 | 0 | 2 |
| name_CO | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_CU | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| name_EDO | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0 | 1 | 1 | 0 | 3 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 |
| name_EPE | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_FAD | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 31 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| name_FE | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| name_FE2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_FMN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_GDP | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_GOL | 8 | 3 | 0 | 11 | 11 | 0 | 6 | 16 | 1 | 0 | 45 | 4 | 2 | 1 | 0 | 0 | 0 | 136 | 0 | 6 | 4 | 4 | 22 | 0 | 11 | 21 | 0 | 11 | 0 | 0 | 2 | 13 | 8 | 0 | 7 | 0 | 2 | 15 | 7 | 8 |
| name_HEM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 65 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 |
| name_K | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_MAN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_MES | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| name_MG | 0 | 2 | 2 | 0 | 11 | 1 | 2 | 5 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 1 | 2 | 0 | 32 | 3 | 1 | 6 | 1 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 9 | 4 | 4 |
| name_MN | 0 | 1 | 0 | 0 | 3 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 5 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 |
| name_MPD | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_NA | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| name_NAD | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 10 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| name_NAG | 0 | 3 | 4 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 8 | 2 | 0 | 3 | 1 | 6 | 1 | 0 | 2 | 0 | 71 | 2 | 1 | 0 | 2 | 1 | 0 | 1 | 2 | 2 | 3 | 2 | 1 |
| name_NAP | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_NDP | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| name_NI | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| name_PEG | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_PG4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| name_PLP | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 1 | 1 | 1 | 0 | 0 | 1 |
| name_PO4 | 2 | 0 | 0 | 0 | 4 | 1 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 6 | 0 | 1 |
| name_SAH | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 |
| name_SEP | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| name_SO4 | 6 | 0 | 0 | 0 | 26 | 2 | 0 | 21 | 2 | 2 | 3 | 2 | 1 | 1 | 0 | 0 | 0 | 12 | 0 | 6 | 0 | 7 | 14 | 2 | 0 | 6 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 0 | 44 | 0 | 3 | 224 | 1 | 14 |
| name_TRS | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| name_ZN | 0 | 1 | 1 | 0 | 10 | 6 | 0 | 0 | 7 | 17 | 0 | 0 | 0 | 16 | 7 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 16 | 0 | 0 | 1 | 0 | 1 | 0 | 9 | 0 | 0 | 2 | 5 | 0 | 2 | 7 | 0 | 148 |